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Ravi Ramamoorthi Ronald L. Graham Professor of Computer Science
Professor CSE Department ; affiliate in ECE |
Office: 4118 EBU3B Phone: 858-822-1483 Fax: 858-534-7029 ravir@cs.ucsd.edu URL: http://www.cs.ucsd.edu/~ravir CV Publications |
| Teaching |
| CSE 167 | Computer Graphics | Winter 2024 | Winter 2023 | Winter 2022 | Winter 2019 | Winter 2017 |
| CSE 168 | Rendering | Spring 2024 | Spring 2021 | Spring 2020 | ||
| CSE 274 | Topics in Computer Graphics | Fall 2022 (Denoising to View Synthesis) | Fall 2021 | Fall 2018 (Samp./Recon. Visual Appearance) | Winter 2018 | Fall 2015 (High Quality Real-Time Rendering) |
| CSE 163 | Advanced Computer Graphics | Spring 2018 | Spring 2017 | Winter 2016 (CSE 190) | Spring 2015 (CSE 190) |
| Selected Awards |
| 2023 Frontiers of Science Award | Certificate | Award Trophy | NeRF of NeRF Trophy | |
| 2019 SIGGRAPH Academy | Plaque | Teapot | CSE Article | |
| 2017 ACM Fellow | CSE Article | SIGGRAPH Article | ACM Announcement | Certificate |
| 2017 edX Prize for Exceptional Online Teaching and Learning (finalist) | edX Blog | UCSD art. | Prize Certificate | |
| 2017 IEEE Fellow | CSE Article | Letter | Certificate | |
| 2016 edX Prize for Exceptional Online Teaching and Learning (inaugural finalist) | edX Blog | UCSD art. | Prize Certificate | |
| 2016 Ronald L. Graham (Endowed) Chair of Computer Science | CSE Article | Announcement Card | ||
| 2011 Okawa Foundation Research Grant | Award Certificate | Website | ||
| 2008 PECASE (Presidential Early Career Awards for Scientists and Engineers) | Award Certificate | Award Photo | Group Photo with President Bush | White House Press Release |
| 2007 SIGGRAPH Significant New Researcher Award for Computer Graphics | Video | Citation | Press Release |
| Selected New Research: 2021-23 |
| Research Summary |
My research group develops the theoretical foundations, mathematical representations and computational models for the visual appearance of objects, digitally recreating or rendering the complexity of natural appearance. Our research program cuts across computer graphics, computer vision and signal processing with applications in sparse reconstruction and frequency analysis, Monte Carlo importance sampling, interactive photorealistic rendering, acquisition and representation of data-driven appearance, volumetric scattering, animation, image and video editing, light-field cameras, physics-based vision and lighting-insensitive recognition. This work has led to more than 150 publications, including more than 75 SIGGRAPH or TOG papers, and has been recognized in 2005 by a Sloan Fellowship and an NSF CAREER award, and in 2007 with an ONR Young Investigator Award and the ACM SIGGRAPH Significant New Researcher Award. More recently, I received a Presidential Early Career Award in a White House ceremony in Dec 2008, and an Okawa Foundation Award in 2011. In Spring 2016, I was appointed the inaugural holder of the Ronald L. Graham Chair of Computer Science. Most recently, I was named a finalist for the inaugural edX prize for exceptional contributions in online teaching and learning (and again in 2017, as the only computer science and only two-time finalist). In 2017, I was also elevated to IEEE fellow for contributions to foundations of computer graphics and computer vision, and to ACM fellow for contributions to computer graphics rendering and physics-based computer vision. In 2019, I was elected to the SIGGRAPH Academy for groundbreaking theoretical work in mathematical representations of visual appearance, and for translating these into computational methods with wide practical impact. Beyond academia, I serve as a rendering research consultant at NVIDIA and am on the technical advisory board at Proprio Vision. A full CV is available.
While my focus has been primarily on academic publication, many of my papers have been refined and later widely implemented in products and commercial applications. For example, my work on spherical harmonic lighting and irradiance environment maps is now widely included in games (such as the Halo series), and is increasingly adopted in movie production (being a critical component of the rendering pipeline in Avatar in 2010, and now an integral part of RenderMan 16, since mid-2011). These ideas are also being used by Adobe for relighting, and are now included in many standard textbooks. My research on importance sampling has inspired a sampling and image-based lighting pipeline that is becoming standard for production rendering (also included in RenderMan 16) and is used for example on the Pixar movie, Monsters University (my papers discussing production use methods are presented at EGSR 2012 and the inaugural JCGT paper). Models for volumetric scattering have been used in demos by NVIDIA, and elsewhere in industry. I also participated in developing the first electronic field guide ; a subsequent iPhone app developed by Prof. Belhumeur and colleagues is now widely used by the public for visual species identification. Most recently, work on sampling and reconstruction for rendering (frequency analysis, adaptive wavelet rendering) has inspired widespread use of Monte Carlo denoising in industry, and been recognized as seminal in a EG STAR report. Subsequent work on real-time physically-based rendering and denoising (axis-aligned filtering for soft shadows) has inspired modern software and hardware real-time AI denoisers, which are now integrated into Optix5 and NVIDIA's RTX chips (2017, 18). As a result, physically-based (raytraced) rendering with denoising is now a reality in both offline and real-time rendering pipelines. Our new Fur Reflectance Model has been used for all animal fur in the 2017 movie War for the Planet of the Apes, nominated for a visual effects Oscar, while new glint models have been used in AutoDesk Fusion 360 and games.
A somewhat out of date summary of research projects is available (the listing of papers below is up to date). I am still interested in many of the same areas, as well as a broader range of topics. My current funding and research interests fall in four main directions: (1) Signal Processing and Sparse Reconstruction of Visual Appearance, with implications across Rendering, Imaging and Animation (see position paper ); (2) A Digital Visual Appearance Pipeline for complex visually rich materials; (3) Physics-Based Computer Vision with realistic reflectance, illumination and light transport, and more generally problems at the vision-graphics interface, including image and video editing and manipulation; (4) Light Field Cameras and RGBD data for depth and reflectance recovery, and higher-level vision/graphics applications. Please see our Light Field Website. I am also interested in collaborations in and outside the field that leverage expertise in these areas.
| Overview Talks and Videos |
Here is a selection of recent invited talks that give an overview of research.
Here is a selection of recent SIGGRAPH and other videos:
| Publications |
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A Free-Space Diffraction BSDF
SIGGRAPH 2024 We derive an edge-based formulation of Fraunhofer diffraction, which is well suited to the common (triangular) geometric meshes used in computer graphics. Our method dynamically constructs a free-space diffraction BSDF by considering the geometry around the intersection point of a ray of light with an object, and we present an importance sampling strategy for these BSDFs. Paper: PDF Supplementary: PDF |
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Practical Error Estimation for Denoised Monte Carlo Image Synthesis
SIGGRAPH 2024 We present a practical global error estimation technique for Monte Carlo ray tracing combined with deep learning based denoising. Our method uses aggregated estimates of bias and variance to determine the squared error distribution of the pixels, and develops a stopping criterion for an error threshold. Paper: PDF |
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Neural Geometry Fields for Meshes
SIGGRAPH 2024 We present Neural Geometry Fields, a neural representation fordiscrete surface geometry represented by triangle meshes. Our ideais to represent the target surface using a coarse set of quadrangularpatches, and add surface details using coordinate neural networksby displacing the patches. Paper: PDF Supplementary: PDF Video: MP4 |
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A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose
SIGGRAPH 2024 In this paper, we leverage the recent 3D Gaussian splatting method to develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution progressively by using monocular depth and projecting pixels back into the 3D world. During construction, we optimize the solution by detecting 2D correspondences between training views and the corresponding rendered images. We develop a unified differentiable pipeline for camera registration and adjustment. Paper: PDF Supplementary: PDF Video: MP4 |
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Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
CVPR 2024 We present Neural Directional Encoding (NDE), a view-dependent appearance encoding of neural radiance fields (NeRF) for rendering specular objects. NDE transfers the concept of feature-grid-based spatial encoding to the angular domain, significantly improving the ability to model high-frequency angular signals. Paper: PDF Supplementary: PDF Video: MP4 |
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What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
CVPR 2024 In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly render every pixel of the full-resolution image during training. Paper: PDF Video: MP4 |
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Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D
CVPR 2024 In this paper, we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative; our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP), but then generalizes to novel vision operators and tasks, such as style transfer, super-resolution, open vocabulary segmentation and image colorization; for some of these tasks, there is no comparable previous 3D method. Paper: PDF |
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Importance Sampling BRDF Derivatives
TOG 2024 In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Paper: PDF |
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Decorrelating ReSTIR Samplers via MCMC Mutations
TOG 2024 We demonstrate how interleaving Markov Chain Monte Carlo (MCMC) mutations with reservoir resampling helps alleviate correlation issues, especially in scenes with glossy materials and difficult-to-sample lighting. Moreover, our approach does not introduce any bias, and in practice we find considerable improvement in image quality with just a single mutation per reservoir sample in each frame. Paper: PDF Video: M4V Supplementary: PDF |
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Conditional Resampled Importance Sampling and ReSTIR
SIGGRAPH Asia 2023 Recent work on generalized resampled importance sampling (GRIS) enables importance-sampled Monte Carlo integration with random variable weights replacing the usual division by probability density. In this paper, we extend GRIS to conditional probability spaces, showing correctness given certain conditional independence between integration variables and their unbiased contribution weights. To show our theory has practical impact, we prototype a modified ReSTIR PT with a final gather pass. This reuses subpaths, postponing reuse at least one bounce along each light path. Paper: PDF Video: MP4 |
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Discontinuity-Aware 2D Neural Fields
SIGGRAPH Asia 2023 We construct a feature field that is discontinuous only across known discontinuity locations and smooth everywhere else, and finally decode the features into the signal value using a shallow multi-layer perceptron. We develop a new data structure based on a curved triangular mesh, and demonstrate applications in rendering, simulation and physics-informed neural networks. Paper: PDF Video: MP4 |
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OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
NeurIPS 2023 We introduce OpenIllumination, a real-world dataset containing over 108K imagesof 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects. Paper: PDF |
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NeRFs: The Search for the Best 3D Representation
Article for ICBS Frontiers of Science Award 2023 At their core, NeRFs describe a new representation of 3D scenes or 3D geometry. as a continuous volume, with volumetric parameters like view-dependent radiance and volume density obtained by querying a neural network. In this article, we briefly review the NeRF representation, and describe the three decades-long quest to find the best 3D representation for view synthesis and related problems. Paper: PDF |
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A Theory of Topological Derivatives for Inverse Rendering of Geometry
ICCV 2023 We introduce a theoretical framework for differentiable surface evolution that allows discrete topology changes through the use of topological derivatives for variational optimization of image functionals. While prior methods for inverse rendering of geometry rely on silhouette gradients for topology changes, such signals are sparse. In contrast, our theory derives topological derivatives that relate the introduction of vanishing holes and phases to changes in image intensity. Paper: PDF |
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Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
ICCV 2023 We propose a novel Factorized Inverse Path Tracing (FIPT) method which utilizes a factored light transport formulation and finds emitters driven by rendering errors. Our algorithm enables accurate material and lighting optimization faster than previous work, and is more effective at resolving ambiguities. Paper: PDF Supplementary: PDF |
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Real-Time Radiance Fields for Single-Image Portrait View Synthesis
SIGGRAPH 2023 We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization. Paper: PDF Video: MP4 |
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NeuSample: Importance Sampling for Neural Materials
SIGGRAPH 2023 In this paper, we evaluate and compare various pdf-learning approaches for sampling spatially-varying neural materials, and propose new variations for three sampling methods: analytic-lobe mixtures, normalizing flows, and histogram predictions. Our versions of normalizing flows and histogram mixtures perform well and can be used in practical rendering systems for adoption of neural materials in production. Paper: PDF |
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Parameter-Space ReSTIR for Differentiable and Inverse Rendering
SIGGRAPH 2023 We develop an algorithm to reuse Monte Carlo gradient samples between gradient iterations, motivated by reservoir-based temporal importance resampling in forward rendering. We reformulate differential rendering integrals in parameter space, developing a new resampling estimator that treats negative functions, and combines these ideas into a reuse algorithm for inverse texture optimization. Paper: PDF |
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NerfDIFF: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
ICML 2023 Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. In this work, we propose NerfDiff, which addresses this issue by distilling the knowledge of a 3D-aware conditional diffusion model (CDM) into NeRF through synthesizing and refining a set of virtual views at test-time. Paper: PDF |
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View Synthesis of Dynamic Scenes based on Deep 3D Mask Volume
PAMI 2023 We introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Paper: PDF Video: MP4 |
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PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN
EGSR 2023 In this work, our goal is to take as input a monocular video of a face, and create an editable dynamic portrait able to handle extreme head poses. The user can create novel viewpoints, edit the appearance, and animate the face. Our method utilizes pivotal tuning inversion (PTI) to learn a personalized video prior froma monocular video sequence. Then we can input pose and expression coefficients to MLPs and manipulate the latent vectors to synthesize different viewpoints and expressions of the subject. Paper: PDF Supplementary: PDF Video: MP4 |
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Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
EGSR 2023 In this paper, we demonstrate a hybrid neural-wavelet PRT solution to high-frequency indirect illumination, including glossy reflection, for relighting with changing view. Specifically, we seek to represent the light transport function in the Haar wavelet basis. For global illumination, we learn the wavelet transport using a small multi-layer perceptron (MLP) applied to a feature field as a function of spatial location and wavelet index, with reflected direction and material parameters being other MLP inputs. Paper: PDF Video: MP4 |
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MesoGAN: Generative Neural Reflectance Shells
CGF 2023 (Presented at EGSR 2023) We introduce MesoGAN, a model for generative 3D neural textures. This new graphics primitive represents mesoscale appearance by combining the strengths of generative adversarial networks (StyleGAN) and volumetric neural field rendering. Paper: PDF Video: MP4 |
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Vision Transformer for NeRF-Based View Synthesis from a Single Input Image
WACV 2023 We leverage both global and local features to form an expressive 3D representation for NeRF-Based view synthesis from a single image. The global features are learned from a vision transformer, while the local features are extracted from a 2D convolutional network. Paper: PDF |
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A Level Set Theory for Neural Implicit Evolution under Explicit Flows
(Best Paper Honorable Mention) ECCV 2022 We present a framework that allows applying deformation operations defined for triangle meshes onto neural implicit surfaces. Our method uses the flow field to deform parametric implicit surfaces by extending the classical theory of level sets. Paper: PDF Project: Code/Video |
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Physically-Based Editing of Indoor Scene Lighting from a Single Image
ECCV 2022 We present a method to edit complex indoor lighting from a single image. We tackle this problem using novel components: a holistic scene reconstruction method that estimates reflectance and parametric 3D lighting, and a neural rendering framework that re-renders the scene from our predictions. We enable light source insertion, removal and replacement. Paper: PDF Project: Code/Video |
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Covector Fluids
SIGGRAPH 2022 We propose a new velocity-based fluid solver derived from a reformulated Euler equation using covectors. Our method generates rich vortex dynamics by an advection process that respects the Kelvin circulation theorem. The numerical algorithm requires only a small local adjustment to existing advection-projection methods and can easily leverage recent advances therein. The resulting solver emulates a vortex method without the expensive conversion between vortical variables and velocities. Paper: PDF Video: MP4 YouTube Code: ZIP |
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Rendering Neural Materials on Curved Surfaces
SIGGRAPH 2022 The goal of this paper is to design a neural material representation capable of correctly handling silhouette effects. We extend the neural network query to take surface curvature information as input, while the query output is extended to return a transparency value in addition to reflectance. We train the new neural representation on synthetic data that contains queries spanning a variety of surface curvatures. We show an ability to accurately represent complex silhouette behavior that would traditionally require more expensive and less flexible techniques, such as on-the-fly geometry displacement or ray marching. Paper: PDF Video: MP4 |
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Spatiotemporal Blue Noise Masks
EGSR 2022 We propose novel blue noise masks that retain high quality blue noise spatially, yet when animated produce values at each pixel that are well distributed over time. By extending spatial blue noise to spatiotemporal blue noise, we overcome the convergence limitations of prior blue noise works, enabling new applications for blue noise distributions. Paper: PDF Supplementary: PDF Code and Data: Github Link |
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
CACM 2022 We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate. Paper: PDF ECCV 20: PDF Video: MP4 YouTube |
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Learning Neural Transmittance for Efficient Rendering of Reflectance Fields
BMVC 2021 We propose a novel method based on precomputed Neural Transmittance Functions to accelerate the rendering of neural reflectance fields. Our neural transmittance functions enable us to efficiently query the transmittance at an arbitrary point in space along an arbitrary ray without tedious ray marching, which effectively reduces the time-complexity of the rendering by up to two orders of magnitude. Paper: PDF |
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Differential Time-Gated Rendering
SIGGRAPH Asia 2021 In this paper, we introduce a new theory of differentiable time-gated rendering that enjoys the generality of differentiating with respect to arbitrary scene parameters. Our theory also allows the design of advanced Monte Carlo estimators capable of handling cameras with near-delta or discontinuous time gates. Paper: PDF Supplementary: ZIP |
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Deep 3D Mask Volume for View Synthesis of Dynamic Scenes
ICCV 2021 The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Paper: PDF Video: MP4 |
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Modulated Periodic Activations for Generalizable Local Functional Representations
ICCV 2021 We present a new representation that generalizes to multiple instances and achieves state-of-the-art fidelity. We use a dual-MLP architecture to encode the signals. A synthesis network creates a functional mapping from a low-dimensional input (e.g., pixel-position) to the output domain (e.g. RGB color). A modulation network maps a latent code corresponding to the target signal to parameters that modulate the periodic activations of the synthesis network. We also propose a local functional representation which enables generalization. Paper: PDF |
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NeuMIP: Multi-Resolution Neural Materials
SIGGRAPH 2021 We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex structures and reflectances. We develop mipmap pyramids of neural textures to address this problem, along with neural offsets. Paper: PDF Video: MP4 |
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Deep Relightable Appearance Models for Animatable Faces
SIGGRAPH 2021 We present a method for building high-fidelity animatable 3D face models that can be posed and rendered with novel lighting environments in real-time. We first train an expensive but generalizable model on point-light illuminations, and use it to generate a training set of high-quality synthetic face images under natural illumination conditions. We then train an efficient model on this augmented dataset. Paper: PDF Video: MP4 |
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Kelvin Transformations for Simulations on Infinite Domains
SIGGRAPH 2021 We introduce a general technique to transform a PDE problem on an unbounded domain to a PDE problem on a bounded domain. Our method uses the Kelvin Transform, which essentially inverts the distance from the origin. We factor the desired solution into the product of an analytically known asymptotic component and another function to solve for, demonstrating Poisson, Laplace and Helmholtz problems. Paper: PDF Video: MP4 |
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Hierarchical Neural Reconstruction for Path Guiding Using Hybrid Path and Photon Samples
SIGGRAPH 2021 We present a hierarchical neural path guiding framework which uses both path and photon samples to reconstruct high-quality sampling distributions. Uniquely, we design a neural network to directly operate on a sparse quadtree, which regresses a high-quality hierarchical sampling distribution. Our novel hierarchical framework enables more fine-grained directional sampling with less memory usage, effectively advancing the practicality and efficiency. This is the follow-up for the Photon-Driven Neural Reconstruction paper below. Paper: PDF Supplementary: PDF |
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Photon-Driven Neural Reconstruction for Path Guiding
TOG 2021 We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the primary input for sampling density reconstruction, which is effective for challenging scenes with strong global illumination. This is the precursor for the Hierarchical Neural Reconstruction paper above. Paper: PDF Supplementary: PDF |
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Vectorization for Fast, Analytic and Differentiable Visibility
TOG 2021 We develop a new rendering method, vectorization, that computes analytic solutions to 2D point-to-region integrals in conventional ray tracing and rasterization pipelines. Our approach revisits beam tracing and maintains all the visible regions formed by intersections and occlusions in the beam (shown leftmost for primary visibility and shadows). Paper: PDF MP4 |
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Neural Light Transport for Relighting and View Synthesis
TOG 2021 We propose a semi-parametric approach for learning a neural representation of the light transport of a scene. The light transport is embedded in a texture atlas of known but possibly rough geometry. We model all non-diffuse and global light transport as residuals added to a physically-based diffuse base rendering. Paper: PDF MP4 |
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NeLF: Neural Light-Transport Field for Portrait View Synthesis and Relighting
EGSR 2021 We present a system for portrait view synthesis and relighting: given multiple portraits, we use a neural network to predict the light-transport field in 3D space, and from the predicted Neural Light-transport Field (NeLF) produce a portrait from a new camera view under a new environmental lighting. Paper: PDF MOV |
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Human Hair Inverse Rendering using Multi-View Photometric Data
EGSR 2021 We introduce a hair inverse rendering framework to reconstruct high-fidelity 3D geometry of human hair, as well as its reflectance,which can be readily used for photorealistic rendering of hair. We demonstrate the accuracy and efficiency of our method using photorealistic synthetichair rendering data. Paper: PDF MOV |
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Uncalibrated, Two Source Photo-Polarimetric Stereo
PAMI 2021 In this paper we present methods for estimating shape from polarisation and shading information, i.e. photo-polarimetric shape estimation, under varying, but unknown, illumination, i.e. in an uncalibrated scenario. We propose several alternative photo-polarimetric constraints that depend upon the partial derivatives of the surface and show how to express them in a unified system of partial differential equations of which previous work is a special case. Paper: PDF |
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OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
CVPR 2021 We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into photorealistic datasets with high-quality ground truth. Paper: PDF Supplementary MP4 |
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Real-Time Selfie Video Stabilization
CVPR 2021 We propose a novel real-time selfie video stabilization method that is completely automatic and runs at 26fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. We also collect a selfie video dataset with 1005 videos for evaluation. Paper: PDF Supplementary MP4 Code+Data |
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Neural Reflectance Fields for Appearance Acquisition
arXiv 2020 We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-connected neural network. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light. We demonstrate that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup. Paper: PDF MP4 |
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Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
NeurIPS 2020 We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs for complex 3D objects and scenes. Paper: PDF |
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Light Stage Super-Resolution: Continuous High-Frequency Relighting
SIGGRAPH Asia 2020 This paper proposes a learning-based solution for the super-resolution of scans of human faces taken from a light stage. Given an arbitrary query light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a virtual light source at the query location. Paper: PDF Video: MP4 |
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Analytic Spherical Harmonic Gradients for Real-Time Rendering with Many Polygonal Area Lights
SIGGRAPH 2020 In this paper, we develop a novel analytic formula for the spatial gradients of the spherical harmonic coefficients for uniform polygonal area lights. The result is a significant generalization, involving the Reynolds transport theorem to reduce the problem to a boundary integral for which we derive a new analytic formula. Paper: PDF Video: MP4 |
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
(Best Paper Honorable Mention)
ECCV 2020 We synthesize novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network whose input is a single continuous 5D coordinate. We use classic differentiable volume rendering to create images from this representation. Paper: PDF Video: MP4 YouTube |
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Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
ECCV 2020 We develop a novel volumetric scene representation for reconstruction from unstructured images. Our representation consists of opacity, surface normal and reflectance voxel grids. We present a novel physically-based differentiable volume ray marching framework to render these scene volumes under arbitrary viewpoint and lighting. Paper: PDF Video: MP4 |
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Deep Multi Depth Panoramas for View Synthesis
ECCV 2020 We propose a learning-based approach for novel view synthesis for multi-camera 360 degree panorama capture rigs. We present a novel scene representation, Multi Depth Panorama (MDP), that consists of multiple RGBD alpha panoramas that represent both scene geometry and appearance. Paper: PDF Video: MP4 |
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Deep Kernel Density Estimation for Photon Mapping
EGSR 2020 We present a novel learning-based photon mapping method that can be used to synthesize photrealistic images with detailed caustics from very sparse photons for scenes with complex diffuse-specular interactions. This is the first deep learning method for denoising particle-based rendering, and can produce global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods. Paper: PDF |
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3D Mesh Processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries
PLOS Computational Biology 2020 Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. Paper: PDF |
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Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting
and SVBRDF from a Single Image
CVPR 2020 We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates physical insights. Paper: PDF |
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Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
CVPR 2020 We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images. We propose adaptive thin volumes (ATVs) for multi-view stereo (MVS). In an ATV, the depth hypothesis of each plane is spatially-varying, which adapts to the uncertainties of previous per-pixel depth predictions. Paper: PDF |
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Learning Video Stabilization Using Optical Flow
CVPR 2020 We propose a novel neural network that infers the per-pixel warp fields for video stabilization from the optical flow fields of the input video. We also propose a pipeline that uses optical flow principal components for motion inpainting and warp field smoothing. Our method gives a 3x speed improvement compared to previous optimization methods. Paper: PDF Video: MP4 |
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Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
CVPR 2020 We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting. We construct high-quality geometry and per-vertex BRDFs. Paper: PDF Video: MP4 |
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Deep Recurrent Network for Fast and Full-Resolution Light Field Deblurring
Sig. Proc. Letters 2019 We propose a novel light field recurrent deblurring network that is trained under 6 degree-of-freedom camera motion-blur model. By combining the real light field captured using Lytro Illum and synthetic light field rendering of 3D scenes from UnrealCV, we provide a large-scale blurry light field dataset to train the network. Paper: PDF |
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A Differential Theory of Radiative Transfer
SIGGRAPH Asia 2019 We introduce a differential theory of radiative transfer, which shows how individual components of the radiative transfer equation (RTE) can be differentiated with respect to arbitrary differentiable changes of a scene. Our theory encompasses the same generality as the standard RTE, allowing differentiation while accurately handling a large range of light transport phenomena such as volumetric absorption and scattering, anisotropic phase functions, and heterogeneity. Paper: PDF |
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Learning Generative Models for Rendering Specular Microgeometry
SIGGRAPH Asia 2019 Rendering specular material appearance is a core problem of computer graphics. We propose a novel direction: learning the high-frequency directional patterns from synthetic or measured examples, by training a generative adversarial network (GAN). A key challenge in applying GAN synthesis to spatially-varying BRDFs is evaluating the reflectance for a single location and direction without the cost of evaluating the whole hemisphere. We resolve this using a novel method for partial evaluation of the generator network. Paper: PDF Video: MP4 |
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Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
ICCV 2019 We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image space is severely under-constrained and produces visible artifacts. Instead, we propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets, and further increases the realism of the textures and shading with an improved CycleGAN network. Paper: PDF |
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Selfie Video Stabilization
PAMI 2019 We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that hasoptimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfievideos can be analyzed using a 3D face model, and background motion can be analyzed using optical flow. Paper: PDF Video (ECCV 18): MP4 |
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Deep View Synthesis from Sparse Photometric Images
SIGGRAPH 2019 In this paper, we synthesize novel viewpoints across a wide range of viewing directions (covering a 60 degree cone) from a sparse set of just six viewing directions. Our method is based on a deep convolutional network trained to directly synthesize new views from the six input views. This network combines 3D convolutions on a plane sweep volume with a novel per-view per-depth plane attention map prediction network to effectively aggregate multi-view appearance. Paper: PDF Supplementary: PDF Video: MOV |
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Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
SIGGRAPH 2019 We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. We propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local lightfields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. Paper: PDF Video: YouTube Code: Project |
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Accurate Appearance Preserving Filtering for Rendering Displacement-Mapped Surfaces
SIGGRAPH 2019 In this paper, we introduce a new method that prefilters displacement maps and BRDFs jointly and constructs SVBRDFs at reduced resolutions. These SVBRDFs preserve the appearance of the input models by capturing both shadowing-masking and interreflection effects. Further, we show that the 6D scaling function can be factorized into a 2D function of surface location and a 4D function of direction. By exploiting the smoothness of these functions, we develop a simple and efficient factorization method that does not require computing the full scaling function. Paper: PDF Video: MP4 Slides: PPTX Code and Data: ZIP |
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Single Image Portrait Relighting
SIGGRAPH 2019 We present a system for portrait relighting: a neural network that takes as input a single RGB image of a portrait taken with a standard cellphone camera in an unconstrained environment, and from that image produces a relit image of that subject as though it were illuminated according to any provided environment map. Paper: PDF Supplementary: PDF Video: M4V |
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Pushing the Boundaries of View Extrapolation with Multiplane Images
CVPR 2019 Best Paper Finalist We present a theoretical analysis showing how the range of views that can be rendered from a multi-plane image (MPI) increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to 4x the lateral viewpoint movement allowed by prior work. Paper: PDF Video: MP4 YouTube: Link With Appendices: PDF |
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Robust Video Stabilization by Optimization in CNN Weight Space
CVPR 2019 We directly model the appearance change as the dense optical flow field of consecutive frames, which leads to a large scale non-convex problem. By solving the problem in the CNN weight space rather than directly for image pixels, we make it a viable formulation for video stabilization. Our method trains the CNN from scratch on each specific input example, and intentionally overfits to produce the best result. Paper: PDF Video: MP4 |
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Deep HDR Video from Sequences with Alternating Exposures
Eurographics 2019 A practical way to generate a high dynamic range (HDR) video using off-the-shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning-based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. Paper: PDF Video: MP4 |
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Analysis of Sample Correlations for Monte Carlo Rendering
Eurographics 2019 Monte Carlo integrators sample the integrand at specific sample point locations. The distribution of these sample points determines convergence rate and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. We aim to provide a comprehensible and accessible overview of techniques developed over the last decade to analyze such correlations. Paper: PDF |
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Connecting Measured BRDFs to Analytic BRDFs by Data-Driven Diffuse-Specular Separation
SIGGRAPH Asia 2018 We propose a novel framework for connecting measured and analytic BRDFs, by separating a measured BRDF into diffuse and specular components. This enables measured BRDF editing, a compact measured BRDF model, and insights in relating measured and analytic BRDFs. We also design a robust analytic fitting algorithm for two-lobe materials. Paper: PDF Supplementary: PDF |
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Learning to Reconstruct Shape and Spatially-Varying Reflectance from a
Single Image
SIGGRAPH Asia 2018 We demonstrate that we can recover non-Lambertian, spatially-varying BRDFs and complex geometry belonging to any arbitrary shape class, from a single RGB image captured under a combination of unknown environment illumination and flash lighting. We achieve this by training a deep neural network to regress shape and reflectance from the image. We incorporate an in-network rendering layer that includes global illumination. Paper: PDF Video: MOV Supplementary: PDF |
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Height-from-Polarisation with Unknown Lighting or Albedo
PAMI Aug 2018 We present a method for estimating surface height directly from a single polarisation image simply by solving a large, sparse system of linear equations. Our method is applicable to dielectric objects exhibiting diffuse and specular reflectance, though lighting and albedo must be known. We relax this requirement by showing that either spatially varying albedo or illumination can be estimated from the polarisation image alone using nonlinear methods. We believe that our method is the first passive, monocular shape-from-x technique that enables well-posed height estimation with only a single, uncalibrated illumination condition. Paper: PDF |
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Selfie Video Stabilization
ECCV 2018 We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background motion can be analyzed using optical flow. Paper: PDF Video: MP4 |
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Rendering Specular Microgeometry with Wave Optics
SIGGRAPH 2018 We design the first rendering algorithm based on a wave optics model, but also able to compute spatially-varying specular highlights with high-resolution detail. We compute a wave optics reflection integral over the coherence area; our solution is based on approximating the phase-delay grating representation of a micron-resolution surface heightfield using Gabor kernels. Our results show both single-wavelength and spectral solution to reflection from common everyday objects, such as brushed, scratched and bumpy metals. Paper: PDF Video: MP4 Supplementary: Supplementary |
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Deep Image-Based Relighting from Optimal Sparse Samples
SIGGRAPH 2018 We present an image-based relighting method that can synthesize scene appearance under novel, distant illumination from the visible hemisphere, from only five images captured under pre-defined directional lights. We show that by combining a custom-designed sampling network with the relighting network, we can jointly learn both the optimal input light directions and the relighting function. Paper: PDF Video: MOV Project: Code, Data |
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Analytic Spherical Harmonic Coefficients for Polygonal Area Lights
SIGGRAPH 2018 We present an efficient closed-form solution for projection of uniform polygonal area lights to spherical harmonic coefficients of arbitrary order, enabling easy adoption of accurate area lighting in PRT systems, with no modifications required to the core PRT framework. Our method only requires computing zonal harmonic (ZH) coefficients, for which we introduce a novel recurrence relation. Paper: PDF Video: MOV Project: Code, Data |
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Deep Adaptive Sampling for Low Sample Count Rendering
EGSR 2018 Recently, deep learning approaches have proven successful at removing noise from Monte Carlo (MC) rendered images at extremely low sampling rates, e.g., 1-4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. We address this issue by proposing a deep learning approach for joint adaptive sampling and reconstruction of MC rendered images with extremely low sample counts. Paper: PDF |
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Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition
EGSR 2018 Intrinsic image decomposition is the process of separating the reflectance and shading layers of an image. In this paper, we propose to systematically address this problem using a deep convolutional neural network (CNN). In addition to directly supervising the network using synthetic images, we train the network by enforcing it to produce the same reflectance for a pair of images of the same real-world scene with different illuminations. Furthermore, we improve the results by incorporating a bilateral solver layer into our system during both training and test stages. Paper: PDF |
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Image to Image Translation for Domain Adaptation
CVPR 2018 We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets. Paper: PDF |
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Learning to See through Turbulent Water
WACV 2018 This paper proposes training a deep convolution neural network to undistort dynamic refractive effects using only a single image. Unlike prior works on water undistortion, our method is trained end-to-end, only requires a single image and does not use a ground truth template at test time. Paper: PDF Project: Code/Data |
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A BSSRDF Model for
Efficient Rendering of Fur with Global Illumination
SIGGRAPH Asia 2017 We present the first global illumination model, based on dipole diffusion for subsurface scattering, to approximate light bouncing between individual fur fibers. We model complex light and fur interactions as subsurface scattering, and use a simple neural network to convert from fur fibers' properties to scattering parameters. Paper: PDF Video: QT |
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Learning to Synthesize a 4D RGBD Light Field from a Single Image
ICCV 2017 We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Paper: PDF Video: MPEG Supplementary: PDF |
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Linear Differential Constraints for Photo-polarimetric Height Estimation
ICCV 2017 In this paper we present a differential approach to photo-polarimetric shape estimation. We propose several alternative differential constraints based on polarisation and photometric shading information and show how to express them in a unified partial differential system. Our method uses the image ratios technique to combine shading and polarisation information in order to directly reconstruct surface height, without first computing surface normal vectors. Paper: PDF |
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Depth and Image Restoration from Light Field in a Scattering Medium
ICCV 2017 Traditional imaging methods and computer vision algorithms are often ineffective when images are acquired in scattering media, such as underwater, fog, and biological tissue. Here, we explore the use of light field imaging and algorithms for image restoration and depth estimation that address the image degradation from the medium. We propose shearing and refocusing multiple views of the light field to recovera single image of higher quality than what is possible from a single view. We demonstrate the benefits of our method through extensive experimental results in a water tank. Paper: PDF |
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An Efficient and Practical Near and Far Field Fur Reflectance Model
SIGGRAPH 2017 We derive a compact BCSDF model for fur reflectance with only 5 lobes. Our model unifies hair and fur rendering, making it easy to implement within standard hair rendering software. By exploiting piecewise analytic integration, our method further enables a multi-scale rendering scheme that transitions between near and far-field rendering smoothly and efficiently for the first time. Paper: PDF Video: QT Papers Trailer: Video |
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Light Field Video Capture Using a Learning-Based Hybrid Imaging System
SIGGRAPH 2017 We develop a hybrid imaging system, adding a standard video camera to a light field camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation, enabling consumer light field videography. Paper: PDF Video: MPEG Code/Data: Project |
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Patch-Based Optimization for Image-Based Texture Mapping
SIGGRAPH 2017 Image-based texture mapping is a common way of producing texture mapsfor geometric models of real-world objects. We propose a novel global patchbasedoptimization system to synthesize the aligned images. Specifically, weuse patch-based synthesis to reconstruct a set of photometrically-consistent aligned images by drawing information from the source images. Our optimization system is simple, flexible, and more suitable for correcting large misalignments than other techniques such as local warping. Paper: PDF Video: MPEG Supplementary: PDF |
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Deep High Dynamic Range Imaging of Dynamic Scenes
SIGGRAPH 2017 Producing a high dynamic range (HDR) image from a set of images with different exposures is a challenging process for dynamic scenes. We use a convolutional neural network (CNN) as our learning model and present and compare three different system architectures to model the HDR merge process. Furthermore, we create a large dataset of input LDR images and their corresponding ground truth HDR images to train our system. Paper: PDF Project Page: Code/Data |
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Light Field Blind Motion Deblurring
CVPR 2017 By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation ofscene geometry. Paper: PDF |
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Robust Energy Minimization for BRDF-Invariant Shape from Light Fields
CVPR 2017 We present a variational energy minimization framework for robust recovery of shape in multiview stereo with complex, unknown BRDFs. While our formulation is general, we demonstrate its efficacy on shape recovery using a single light field image, where the microlens array may be considered as a realization of a purely translational multiview stereo setup. Our formulation automatically balances contributions from texture gradients, traditional Lambertian photoconsistency, an appropriate BRDF-invariant PDE and a smoothness prior. Paper: PDF Supplementary |
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Gradient-Domain Vertex Connection and Merging
EGSR 2017 We present gradient-domain vertex connection and merging (G-VCM), a new gradient-domain technique motivated by primal domain VCM. Our method enables robust gradient sampling in the presence of complex transport, such as specular-diffuse-specular paths, while retaining the denoising power and fast convergence of gradient-domain bidirectional path tracing. Paper: PDF |
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Multiple Axis-Aligned Filters for Rendering of Combined Distribution Effects
EGSR 2017 We present a novel filter for efficient rendering of combined effects, involving soft shadows and depth of field, with global (diffuse indirect) illumination. We approximate the wedge spectrum with multiple axis-aligned filters, marrying the speed of axis-aligned filtering with an even more accurate (compact and tighter) representation than sheared filtering. Paper: PDF Video: AVI |
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SVBRDF-Invariant Shape and Reflectance Estimation from a Light-Field Camera
PAMI 2017 Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals or plastics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Paper: PDF |
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Antialiasing Complex Global Illumination Effects in Path-space
TOG 2017 We present the first method to efficiently predict antialiasing footprints to pre-filter color-, normal-, and displacement-mapped appearance in the context of multi-bounce global illumination. We derive Fourier spectra for radiance and importance functions that allow us to compute spatial-angular filtering footprints at path vertices. Paper: PDF |
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Learning-Based View Synthesis for Light Field Cameras
SIGGRAPH Asia 2016 we propose a novel learning-based approach to synthesize new views from a sparse set of input views for light field cameras. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. Paper: PDF Video: MPEG Dataset: Project Page |
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Minimal BRDF Sampling for Two-Shot Near-Field Reflectance Acquisition
SIGGRAPH Asia 2016 We develop a method to acquire the BRDF of a homogeneous flat sample from only two images, taken by a near-field perspective camera, and lit by a directional light source. We develop a mathematical framework to estimate error from a given set of measurements, including the use of multiple measurements in an image simultaneously, as needed for acquisition from near-field setups. Paper: PDF Supplementary: PDF Comparison Video: QT |
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Downsampling Scattering Parameters for Rendering Anisotropic Media
SIGGRAPH Asia 2016 Volumetric micro-appearance models have provided remarkably high-quality renderings, but are highly data intensive and usually require tens of gigabytes in storage. We introduce a joint optimization of single-scattering albedos and phase functions to accurately downsample heterogeneous and anisotropic media. Paper: PDF Video: MPEG |
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Photometric Stereo in a Scattering Medium
PAMI 2016 Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biologicaltissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scatteredback from the medium (backscatter). Here we make three contributions to address the key modes of light propagation, under thecommon single scattering assumption for dilute media. Paper: PDF |
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Linear Depth Estimation from an Uncalibrated, Monocular Polarisation Image
ECCV 2016 We present a method for estimating surface height directly from a single polarisation image simply by solving a large, sparse system of linear equations. To do so, we show how to express polarisation constraints as equations that are linear in the unknown depth. Our method is applicable to objects with uniform albedo exhibiting diffuse and specular reflectance. We believe that our method is the first monocular, passive shape-from-x technique that enables well-posed depth estimation with only a single, uncalibrated illumination condition. Paper: PDF |
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A 4D Light-Field Dataset and CNN Architectures for Material Recognition
ECCV 2016 We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7% boost compared with 2D image classification. Paper: PDF HTML Comparison dataset (2D thumbnail) Full Dataset (16GB) |
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Sparse Sampling for Image-Based SVBRDF Acquisition
Material Appearance Modeling Workshop, 2016 We acquire the data-driven spatially-varying (SV)BRDF of a flat sample from only a small number of images (typically 20). We generalize the homogenous BRDF acquisition work of Nielsen et al., who derived an optimal minmal set of lighting/view directions. We demonstrate our method on SVBRDF measurements of new flat materials, showing thatfull data-driven SVBRDF acquisition is now possible from a sparse set of only about 20 light-view pairs. Paper: PDF |
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Position-Normal Distributions for Efficient Rendering of Specular Microstructure
SIGGRAPH 2016 Specular BRDF rendering traditionally approximates surface microstructure using a smooth normal distribution, but this ignores glinty effects, easily observable in the real world. We treat a specular surface as a four-dimensional position-normal distribution, and fit this distribution using millions of 4D Gaussians, which we call elements. This leads to closed-form solutions to the required BRDF evaluation and sampling queries, enabling the first practical solution to rendering specular microstructure. Paper: PDF Video: MP4 Press: UCSD PhysOrg Digital Trends Eureka Alert Tech Crunch |
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Shape Estimation from Shading, Defocus, and Correspondence Using Light-Field
Angular Coherence
PAMI 2016 We show that combining all three sources of information: defocus, correspondence, and shading, outperforms state-of-the-art light-field depth estimation algorithms in multiple scenarios. Paper: PDF |
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SVBRDF-Invariant Shape and Reflectance Estimation from Light-Field Cameras
CVPR 2016 Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals, plastics or ceramics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Paper: PDF |
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Depth from Semi-Calibrated Stereo and Defocus
CVPR 2016 In this work, we propose a multi-camera system where we combine a main high-quality camera with two low-res auxiliary cameras. Our goal is, given the low-res depth map from the auxiliary cameras, generate a depth map from the viewpoint of the main camera. Ours is a semi-calibrated system, where the auxiliary stereo cameras are calibrated, but the main camera has an interchangeable lens, and is not calibrated beforehand. Paper: PDF |
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Depth Estimation with Occlusion Modeling Using Light-field Cameras
PAMI 2016 In this paper, an occlusion-aware depth estimation algorithm is developed; the method also enables identification of occlusion edges,which may be useful in other applications. It can be shown that although photo-consistency is not preserved for pixels at occlusions, it still holds in approximately half the viewpoints. Moreover, the line separating the two view regions (occluded object vs. occluder) has the same orientation as that of the occlusion edge in the spatial domain. By ensuring photo-consistency in only the occluded view region, depth estimation can be improved. Paper: PDF |
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Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects
ACM Trans. Graphics Dec 2015 We present a new approach for fast sheared filtering on the GPU. Our algorithm factors the 4D sheared filter into four 1D filters. We derive complexity bounds for our method, showing that the per-pixel complexity is reduced from O(n^2 l^2) to O(nl), where n is the linear filter width (filter size is O(n^2)) and l is the (usually very small) number of samples for each dimension of the light or lens per pixel (spp is l2). We thus reduce sheared filtering overhead dramatically. Paper: PDF Video: MPEG |
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Physically-Accurate Fur Reflectance: Modeling, Measurement and Rendering
SIGGRAPH Asia 2015 In this paper, we develop a physically-accurate reflectance model for fur fibers. Based on anatomical literature and measurements, we develop a double cylinder model for the reflectance of a single fur fiber, where an outer cylinder represents the biological observation of a cortex covered by multiple cuticle layers, and an inner cylinder represents the scattering interior structure known as the medulla. Paper: PDF MS thesis of Chiwei Tseng Data |
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Anisotropic Gaussian Mutations for Metropolis Light Transport through
Hessian-Hamiltonian Dynamics
SIGGRAPH Asia 2015 We present a Markov Chain Monte Carlo(MCMC) rendering algorithm that extends Metropolis Light Transport by automatically and explicitly adapting to the local shape of the integrand, thereby increasing the acceptance rate. Our algorithm characterizes the local behavior of throughput in path space using its gradient as well as its Hessian. In particular, the Hessian is able to capture the strong anisotropy of the integrand. Paper: PDF |
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On Optimal, Minimal BRDF Sampling for Reflectance Acquisition
SIGGRAPH Asia 2015 In this paper, we address the problem of reconstructing a measured BRDF from a limited number of samples. We present a novel mapping of the BRDF space, allowing for extraction of descriptive principal components from measured databases, such as the MERL BRDF database. We optimize for the best sampling directions, and explicitly provide the optimal set of incident and outgoing directions in the Rusinkiewicz parameterizationfor n = 1; 2; 5; 10; 20 samples. Based on the principal components, we describe a method for accurately reconstructing BRDF data from these limited sets of samples. Paper: PDF |
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Photometric Stereo in a Scattering Medium
ICCV 2015 Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biological tissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scattered back from the medium (backscatter). Here we make three contributions to address the key modes of light propagation, under the common single scattering assumption for dilute media. Paper: PDF |
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Occlusion-aware Depth Estimation Using Light-field Cameras
ICCV 2015 In this paper, we develop a depth estimation algorithm for light field cameras that treats occlusion explicitly; the method also enables identification of occlusion edges, which may be useful in other applications. We show that, although pixels at occlusions do not preserve photo-consistency in general, they are still consistent in approximately half the viewpoints. Paper: PDF |
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Oriented Light-Field Windows for Scene Flow
ICCV 2015 For Lambertian surfaces focused to the correct depth, the 2D distribution of angular rays from a pixel remains consistent. We build on this idea to develop an oriented 4D light-field window that accounts for shearing(depth), translation (matching), and windowing. Our main application is to scene flow, a generalization of optical flow to the 3D vector field describing the motion of each point in the scene. Paper: PDF |
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Depth Estimation and Specular Removal for Glossy Surfaces Using Point and Line Consistency with Light-Field Cameras
PAMI 2015 (to appear) Light-field cameras have now become available in both consumer and industrial applications, and recent papers havedemonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimationmethods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. In this paper, wepresent a novel theory of the relationship between light-field data and reflectance from the dichromatic model. Paper: PDF |
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Depth from Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
CVPR 2015 Using shading information is essential to improve shape estimation from light field cameras. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth. We show that the angular pixels have angular coherence, which exhibits three properties: photoconsistency, depth consistency, and shading consistency. Paper: PDF |
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Probabilistic Connections for Bidirectional Path Tracing
Computer Graphics Forum (EGSR) 2015 Bidirectional path tracing (BDPT) with Multiple Importance Sampling is one of the most versatile unbiased rendering algorithms today. BDPT repeatedly generates sub-paths from the eye and the lights, which are connected for each pixel and then discarded. Unfortunately, many such bidirectional connections turn out to have low contribution to the solution. Our key observation is that we can importance sample connections to an eye sub-path by considering multiple light sub-paths at once and creating connections probabilistically. Paper: PDF |
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Filtering Environment Illumination for Interactive Physically-Based Rendering in Mixed Reality
EGSR 2015 We propose accurate filtering of a noisy Monte-Carlo image using Fourier analysis. Our novel analysis extends previous works by showing that the shape of illumination spectra is not always a line or wedge, as in previous approximations, but rather an ellipsoid. Our primary contribution is an axis-aligned filtering scheme that preserves the frequency content of the illumination.We also propose a novel application of our technique to mixed reality scenes, in which virtual objects are inserted into a real video stream so as to become indistinguishable from the real objects. Paper: PDF Video: MP4 Supplementary: PDF |
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Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering
EUROGRAPHICS 2015 In this paper we survey recent advances in adaptive sampling and reconstruction. We distinguish between a priori methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and a posteriori methods that apply statistical techniques to sets of samples. Paper: PDF |
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A Light Transport Framework for Lenslet Light Field Cameras
ACM Transactions on Graphics (Apr 2015). It is often stated that there is a fundamental tradeoff between spatial and angular resolution of lenslet light field cameras, but there has been limited understanding of this tradeoff theoretically or numerically. In this paper, we develop a light transport framework for understanding the fundamental limits of light field camera resolution. Paper: PDF Supplementary Images: PDF |
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City Forensics: Using Visual Elements to Predict Non-Visual City Attributes
IEEE TVCG [SciVis 2014]. Honorable Mention for Best Paper Award We present a method for automatically identifying and validating predictive relationships between the visual appearance of a city and its non-visual attributes (e.g. crime statistics, housing prices, population density etc.). We also test human performance for predicting theft based on street-level images and show that our predictor outperforms this baseline with 33% higher accuracy on average. Paper: PDF |
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High-Order Similarity Relations in Radiative Transfer
SIGGRAPH 2014. Radiative transfer equations (RTEs) with different scattering parameters can lead to identical solution radiance fields. Similarity theory studies this effect by introducing a hierarchy of equivalence relations called similarity relations. Unfortunately, given a set of scattering parameters, it remains unclear how to find altered ones satisfying these relations, significantly limiting the theory's practical value. This paper presents a complete exposition of similarity theory, which provides fundamental insights into the structure of the RTE's parameter space. To utilize the theory in its general high-order form, we introduce a new approach to solve for the altered parameters including the absorption and scattering coefficients as well as a fully tabulated phase function. Paper: PDF Video (MPEG) |
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Rendering Glints on High-Resolution Normal-Mapped Specular Surfaces
SIGGRAPH 2014. Complex specular surfaces under sharp point lighting show a fascinating glinty appearance, but rendering it is an unsolved problem. Using Monte Carlo pixel sampling for this purpose is impractical: the energy is concentrated in tiny highlights that take up a minuscule fraction of the pixel. We instead compute an accurate solution using a completely different deterministic approach. Paper: PDF Video (MPEG) |
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Factored Axis-Aligned Filtering for Rendering Multiple Distribution Effects
SIGGRAPH 2014. We propose an approach to adaptively sample and filter for simultaneously rendering primary (defocus blur) and secondary (soft shadows and indirect illumination) distribution effects, based on a multi-dimensional frequency analysis of the direct and indirect illumination light fields, and factoring texture and irradiance. Paper: PDF Video (MPEG) |
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Discrete Stochastic Microfacet Models
SIGGRAPH 2014. This paper investigates rendering glittery surfaces, ones which exhibitshifting random patterns of glints as the surface or viewermoves. It applies both to dramatically glittery surfaces that containmirror-like flakes and also to rough surfaces that exhibit more subtlesmall scale glitter, without which most glossy surfaces appeartoo smooth in close-up. Inthis paper we present a stochastic model for the effects of randomsubpixel structures that generates glitter and spatial noise that behavecorrectly under different illumination conditions and viewingdistances, while also being temporally coherent so that they lookright in motion. Paper: PDF Video (MPEG) |
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Depth Estimation for Glossy Surfaces with Light-Field Cameras
ECCV 14 Workshop Light Fields Computer Vision. Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to support both specular and diffuse scenes. Paper: PDF |
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User-Assisted Video Stabilization
EGSR 2014. We present a user-assisted video stabilization algorithm that is able to stabilize challenging videos. First, we cluster tracks and visualize them on the warped video. The user ensures that appropriate tracks are selected by clicking on track clusters to include or exclude them. Second, the user can directly specify how regions in the output video should look by drawing quadrilaterals to select and deform parts of the frame. Paper: PDF Video (MPEG) |
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Depth from Combining Defocus and Correspondence Using Light-Field Cameras
ICCV 2013. Light-field cameras have recently become available to the consumer market. An array of micro-lenses captures enough information that one can refocus images after acquisition, as well as shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. Thus, depth cues from both defocus and correspondence are available simultaneously in a single capture, and we show how to exploit both by analyzing the EPI. Paper: PDF Video (MPEG) |
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External mask based depth and light field camera
ICCV 13 Workshop Consumer Depth Cameras for Vision. We present a method to convert a digital single-lens reflex (DSLR) camera into a high-resolution consumer depth and light-field camera by affixing an external aperture mask to the main lens. Compared to the existing consumer depth and light field cameras, our camera is easy to construct with minimal additional costs, and our design is camera and lens agnostic. The main advantage of our design is the ease of switching between an SLR camera and a native resolution depth/light field camera. We also do not need to modify the internals of the camera or the lens. Paper: PDF Video (MPEG) |
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Axis-Aligned Filtering for Interactive Physically-Based Diffuse Indirect
Lighting
SIGGRAPH 2013. We introduce an algorithm for interactive rendering of physically-based global illumination, based on a novel frequency analysis of indirect lighting. Our method combines adaptive sampling byMonte Carlo ray or path tracing, using a standard GPU-accelerated raytracer, with real-time reconstruction of the resulting noisy images. Paper: PDF Video (MPEG) |
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Modular Flux Transfer: Efficient Rendering of High-Resolution Volumes with
Repeated Structures
SIGGRAPH 2013. Common volumetric materials (fabrics, finished wood, synthesized solid textures) are structured, with repeated patterns approximated by tiling a small number of exemplar blocks. In this paper, we introduce a precomputation-based rendering approach for such volumetric media with repeated structures based on a modular transfer formulation. We model each exemplar block as a voxel grid and precompute voxel-to-voxel, patch-to-patch, and patch-to-voxel flux transfer matrices. Paper: PDF Video (MPEG) |
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What Object Motion Reveals About Shape with Unknown BRDF and Lighting
CVPR 2013. We present a theory that addresses the problem of determining shape from the (small or differential) motion of an object with unknown isotropic reflectance, under arbitrary unknown distant illumination, for both orthographic and perpsective projection. Our theory imposes fundamental limits on the hardness of surface reconstruction, independent of the method involved. Under orthographic projection, we prove that three differential motions suffice to yield an invariant that relates shape to image derivatives, regardless of BRDF and illumination. Under perspective projection, we show that four differential motions suffice to yield depth and a linear constraint on the surface gradient. Paper: PDF |
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Automatic Cinemagraph Portraits
EGSR 2013. Cinemagraphs are a popular new type of visual media that lie in-between photos and video; some parts of the frame are animated and loop seamlessly, while other parts of the frame remain completely still. Cinemagraphs are especially effective for portraits because they capture the nuances of our dynamic facial expressions. We present a completely automatic algorithm for generating portrait cinemagraphs from a short video captured with a hand-held camera. Paper: PDF Video (MPEG) |
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Interactive Albedo Editing in Path-Traced Volumetric Materials
TOG 2013 (April Cover Image). In this paper, we develop an editing algorithm that enables a material designer to set the local (single-scattering) albedo coefficients interactively, and see an immediate update of the emergent appearance in the image. We also extend the technique to editing the overall mean free path of the material. This is a difficult problem, since the function from materials to pixel values is neither linear nor low-order polynomial. Paper: PDF Video (MPEG) |
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Gloss Perception in Painterly and Cartoon Rendering
TOG 2013. We describe the first study of material perception in stylized images (specifically painting and cartoon) and use non-photorealistic rendering algorithms to evaluate how such stylization alters the perception of gloss. This mapping allows users of NPR algorithms to predict, and correct for, the perception of gloss in their images. Paper: PDF |
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Sharpening Out of Focus Images using High-Frequency Transfer
EuroGraphics 2013. We propose a new method to sharpen out-of-focus images, that uses a similar but different assisting sharp image provided by the user (such as multiple images of the same subject in different positions captured using a burst of photographs). We demonstrate sharpened results on out-of-focus images in macro, sports, portrait and wildlife photography. Paper: PDF Video (MPEG) Supplementary HTML |
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On Differential Photometric Reconstruction for Unknown, Isotropic BRDFs
PAMI 2013. This paper presents a comprehensive theory of photometric surface reconstruction from image derivatives, in the presence of a general, unknown isotropic BRDF. We derive precise topological classes up to which the surface may be determined and specify exact priors for a full geometric reconstruction, for both shape from shading and photometric stereo. Paper: PDF |
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Compressive Structured Light for Recovering Inhomogeneous Participating
Media PAMI 2013. We propose a new method named compressive structured light for recovering inhomogeneous participating media. Whereas conventional structured light methods emit coded light patterns onto the surface of an opaque object to establish correspondence for triangulation, compressive structured light projects patterns into a volume of participating medium to produce images which are integral measurements of the volume density along the line of sight. Paper: PDF |
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Axis-Aligned Filtering for Interactive Sampled Soft Shadows
Siggraph Asia 2012. We develop a simple and efficient method for soft shadows from planar area light sources, based on explicit occlusion calculation by raytracing, followed by adaptive image-space filtering. Since the method is based on Monte Carlo sampling, it is accurate. Since the filtering is in image-space, it adds minimal overhead and can be performed at real-time frame rates. We obtain interactive speeds, using the Optix GPU raytracing framework. Our technical approach derives from recent work on frequency analysis and sheared pixel-light filtering for offline soft shadows. While sample counts can be reduced dramatically, the sheared filtering step is slow, adding minutes of overhead. We develop the theoretical analysis to instead consider axis-aligned filtering, deriving the sampling rates and filter sizes. Paper: PDF Video (MPEG) Source Code Chrome users may need to open these links in a new window. |
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Frequency-Space Decomposition and Acquisition of Light Transport under
Spatially Varying Illumination
ECCV 2012. We show that, under spatially varying illumination, the light transport of diffuse scenes can be decomposed into direct, near-range (subsurface scattering and local inter-reflections) and far range transports (diffuse inter-reflections). We show that these three component transports are redundant either in the spatial or the frequency domain and can be separated using appropriate illumination patterns, achieving a theoretical lower bound. Paper: PDF |
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A Theory of Monte Carlo Visibility Sampling
ACM TOG Aug 2012. We develop a comprehensive theoretical analysis of different sampling patterns for Monte Carlo visibility. In particular, we show the benefits of uniform jitter sampling over stratified in some cases, and demonstrate that it produces the lowest variance for linear lights. Surprisingly, the best pattern depends on the shape of the light source for area lights, with uniform jitter preferred for circular lights and stratified for square lights. Paper: PDF Video: MPEG4 Talk: PPT |
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Selectively De-Animating Video
SIGGRAPH 2012. We present a semi-automated technique for selectively de-animating video to remove the large-scale motions of one or more objects so that other motions are easier to see. Our technique enables a number of applications such as clearer motion visualization, simpler creation of artistic cinemagraphs (photos that include looping motions in some regions), and new ways to edit appearance and complicated motion paths in video by manipulating a de-animated representation. Paper: PDF Video: MPEG4 Teaser Video: MPEG4 |
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Analytic Tangent Irradiance Environment Maps for Anisotropic Surfaces
EGSR 2012. We extend spherical harmonic irradiance maps to anisotropic surfaces, replacing Lambertian reflectance with the diffuse term of the popular Kajiya-Kay model. We show that the terms decay even more rapidly than for Lambertian reflectance. Existing code for irradiance environment maps can be trivially adapted for real-time rendering with tangent irradiance maps. We also demonstrate an application to offline rendering of the diffuse component of fibers, using our formula as a control variate for Monte Carlo sampling. Paper: PDF |
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Importance Sampling of Reflection from Hair Fibers
JCGT 2012 (Inaugural Article). Hair and fur are increasingly important visual features in production rendering, and physically-based light scattering models are now commonly used. In this paper, we enable efficient Monte Carlo rendering of specular reflections from hair fibers. We describe a simple and practical importance sampling strategy for the reflection term in the Marschner hair model. Our method has been widely used in production for more than a year, and complete pseudocode is provided. Paper: PDF |
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Real-Time Rendering of Rough Refraction
IEEE TVCG Feb 2012. We present an algorithm to render objects made of transparent materials with rough surfaces in real-time, under distant illumination. Rough surfaces cause wide scattering as light enters and exits objects, which significantly complicates the rendering of such materials. We approximate the Bidirectional Transmittance Distribution Function (BTDF), using spherical Gaussians. We also propose two extensions, to support spatially-varying roughness and local lighting on thin objects. Paper: PDF Youtube Video (from I3D 2011) |
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From the Rendering Equation to Stratified Light Transport Inversion International Journal of Computer Vision, 2012 In this work, we explore a theoretical analysis of inverse light transport, relating it to its forward counterpart, expressed in the form of the rendering equation. We show the existence of an inverse Neumann series, that zeroes out the corresponding physical bounces of light, which we refer to as stratified light transport inversion. Our practical application is to radiometric compensation, where we seek to project patterns onto real-world surfaces, undoing the effects of global illumination. Paper: PDF |
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Practical Filtering for Efficient Ray-Traced Directional Occlusion
SIGGRAPH Asia 2011 Ambient occlusion and directional (spherical harmonic) occlusion have become a staple of production rendering, but are expensive to compute. We give a frequency analysis of shadow light fields using distant illumination with a general BRDF and normal mapping, allowing us to share ray information even among complex receivers. We also present a new rotationally-invariant filter that easily handles samples spread over a large angular domain. Our method can deliver 4x speed up for scenes that are computationally bound by ray tracing costs. Paper: PDF Supplementary Animations AVI |
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Sparse Reconstruction of Visual Appearance for Computer Graphics and Vision SPIE Keynote, Wavelets and Sparsity XIV 2011 A broad range of problems in computer graphics rendering, appearance acquisition for graphics and vision, and imaging, involve sampling, reconstruction, and integration of high-dimensional (4D-8D) signals. We argue that dramatically sparser sampling and reconstruction of these signals is possible, before the full dataset is acquired or simulated. Our key idea is to exploit the structure of the data that often lies in lower-frequency, sparse, or low-dimensional spaces. Paper: PDF |
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What an Image Reveals About Material Reflectance
ICCV 2011 We derive precise conditions under which material reflectance properties may be estimated from a single image of a homogeneous curved surface (canonically a sphere), lit by a directional source. Based on the observation that light is reflected along certain (a priori unknown) preferred directions such as the half-angle, we propose a semiparametric BRDF abstraction that lies between purely parametric and purely data-driven models. While it is well-known that fitting multi-lobe BRDFs may be ill-posed under certain conditions, prior to this work, precise results for the well-posedness of BRDF estimation had remained elusive. Paper: PDF |
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On the Duality of Forward and Inverse Light Transport
PAMI 2011, ECCV 2010 Inverse light transport seeks to undo global illumination effects, such as interreections, that pervade images of most scenes. This paper presents the theoretical and computational foundations for inverse light transport as a dual of forward rendering. We demonstrate two practical applications, namely, separation of individual bounces of the light transport and fast projector radiometric compensation to display images free of global illumination artifacts in real-world environments. Paper: PAMI ECCV Tech Report Video |
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Data-Driven Elastic Models for Cloth: Modeling and Measurement SIGGRAPH 2011 Cloth often has complicated nonlinear, anisotropic elastic behavior due to its woven pattern and fiber properties. However, most current cloth simulation techniques simply use linear and isotropic elastic models with manually selected stiffness parameters. Such simple simulations do not allow differentiating the behavior of distinct cloth materials such as silk or denim, and they cannot model most materials with fidelity to their real-world counterparts. In this paper, we present a data-driven technique to more realistically animate cloth. These measurements can be used in most cloth simulation systems to create natural and realistic clothing wrinkles and shapes, for a range of different materials. Paper: PDF Video |
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Illumination Decomposition for Material Recoloring with Consistent Interreflections SIGGRAPH 2011 Changing the color of an object is a basic image editing operation, but a high quality result must also preserve natural shading. A common approach is to first compute reflectance and illumination intrinsic images. Reflectances can then be edited independently, and recomposed with the illumination. However, manipulating only the reflectance color does not account for diffuse interreflections, and can result in inconsistent shading in the edited image. We propose an approach for further decomposing illumination into direct lighting, and indirect diffuse illumination from each material. Paper: PDF |
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Frequency Analysis and Sheared Filtering for Shadow Light Fields of Complex Occluders TOG 2011 Monte Carlo ray tracing of soft shadows produced by area lighting and intricate geometries, such as the shadows through plant leaves or arrays of blockers, is a critical challenge. This article develops an efficient diffuse soft shadow technique for mid to far occluders that relies on a new 4D cache and sheared reconstruction filter. Our analysis subsumes convolution soft shadows for parallel planes as a special case. Paper: PDF Supplemental Animations |
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Optimizing Environment Maps for Material Depiction
EGSR 2011 We present an automated system for optimizing and synthesizing environment maps that enhance the appearance of materials in a scene. We first identify a set of lighting design principles for material depiction. Each principle specifies the distinctive visual features of a material and describes how environment maps can emphasize those features. We express these principles as linear or quadratic image quality metrics, and present a general optimization framework to solve for the environment map that maximizes these metrics. We accelerate metric evaluation using an approach dual to precomputed radiance transfer (PRT). Paper: PDF Video of Quality Metric |
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A Theory of Differential Photometric Stereo for Unknown Isotropic BRDFs
CVPR 2011 We present a comprehensive theory of photometric surface reconstruction from image derivatives. For unknown isotropic BRDFs, we show that two measurements of spatial and temporal image derivatives, under unknown light sources on a circle, suffice to determine the surface. This result is the culmination of a series of fundamental observations. Our theoretical results are illustrated with several examples on synthetic and real data. Paper: PDF Tech Report |
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Real-Time Rough Refraction
I3D 2011 Best Paper Award We present an algorithm to render objects of transparent materials with rough surfaces in real-time, under distant illumination. Rough surfaces cause wide scattering as light enters and exits objects, which significantly complicates the rendering of such materials. We approximate the Bidirectional Transmittance Distribution Function (BTDF), using spherical Gaussians, suitable for real-time estimation of environment lighting using pre-convolution. Paper: PDF Youtube Video |
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Multi-Resolution Isotropic Strain Limiting
SIGGRAPH Asia 2010. In this paper we describe a fast strain-limiting method that allows stiff, incompliant materials to be simulated efficiently. Unlike prior approaches, which act on springs or individual strain components, this method acts on the strain tensors in a coordinate-invariant fashion allowing isotropic behavior. For triangulated surfaces in three-dimensional space, we also describe a complementary edge-angle-limiting method to limit out-of-plane bending. To accelerate convergence, we also propose a novel multi-resolution algorithm that enforces fitted limits at each level of a non-conforming hierarchy. Paper: PDF Video |
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Example-Based Wrinkle Synthesis for Clothing Animation
SIGGRAPH 2010. This paper describes a method for animating the appearance of clothing, such as pants or a shirt, that fits closely to a figure's body. Based on the observation that the wrinkles in close-fitting clothing behave in a predominantly kinematic fashion, we have developed an example-based wrinkle synthesis technique. Our method drives wrinkle generation from the pose of the figure's kinematic skeleton. This approach allows high quality clothing wrinkles to be combined with a coarse cloth simulation that computes the global and dynamic aspects of the clothing motion. Further, the combined system runs at interactive rates, making it suitable for applications where high-resolution offline simulations would not be a viable option. Paper: PDF Video Papers Trailer |
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Sparsely Precomputing the Light Transport Matrix for Real-Time Rendering
EGSR 2010. Precomputation-based methods have enabled real-time rendering with natural illumination, all-frequency shadows, and global illumination. However, a major bottleneck is the precomputation time, that can take hours to days. While the final real-time data structures are typically heavily compressed with clustered principal component analysis and/or wavelets, a full light transport matrix still needs to be precomputed for a synthetic scene, often by exhaustive sampling and raytracing. In this paper, we show that the precomputation can be made much more efficient by adaptive and sparse sampling of light transport. We demonstrate sparse sampling and precomputation 5x faster than previous methods. Paper: PDF (if no EG DL subscription see TR) Tech Report Video |
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Adaptive Wavelet Rendering
SIGGRAPH Asia 2009. Effects such as depth of field, area lighting, antialiasing and global illumination require evaluating a complex high-dimensional integral at each pixel of an image. We develop a new adaptive rendering algorithm that greatly reduces the number of samples needed for Monte Carlo integration. Our method renders directly into an image-space wavelet basis. Moreover, the method introduces minimal overhead, and can be efficiently included in an optimized ray-tracing system. Paper: PDF (44M) |
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Removing Image Artifacts Due to Dirty Camera Lenses and Thin Occluders
SIGGRAPH Asia 2009. There are often physical layers between the scene and the imaging system. For example, the lenses of consumer digital cameras often accumulate various types of contaminants over time (e.g., fingerprints, dust, dirt). Also, photographs are often taken through a layer of thin occluders (e.g., fences, meshes, window shutters, curtains, tree branches) which partially obstruct the scene. We show that both effects can be described by a single image formation model, and removed from digital photographs. Paper: PDF |
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Precomputation-Based Rendering
Foundations and Trends in Computer Graphics and Vision 3(4), 281-369. Precomputation-based relighting and radiance transfer has a long history with a spurt of renewed interest, including adoption in commercial video games, due to recent mathematical developments and hardware advances. In this survey, we describe the mathematical foundations, history, current research and future directions for precomputation-based rendering. Paper: PDF |
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Frequency Analysis and Sheared Reconstruction for Rendering Motion Blur
SIGGRAPH 09. Motion blur is crucial for high-quality rendering, but is also very expensive. Our first contribution is a frequency analysis of motionblurred scenes, including moving objects, specular reflections, and shadows. We show that motion induces a shear in the frequency domain, and that the spectrum of moving scenes is usually contained in a wedge. This allows us to compute adaptive space-time sampling rates, to accelerate rendering. Our second contribution is a novel sheared reconstruction filter that is aligned to the first-order direction of motion and enables even lower sampling rates. Paper: PDF |
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Moving Gradients: A Path-Based Method for Plausible Image Interpolation
SIGGRAPH 09. We describe a method for plausible interpolation of images, with a wide range of applications like temporal up-sampling for smooth playback of lower frame rate video, smooth view interpolation, and animation of still images. We develop a novel path-based framework, greater flexibility via transition points, new ways to handle visibility, and Poisson reconstruction to produce smooth interpolations. Paper: PDF Video (QT 55M) |
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An Empirical BSSRDF Model
SIGGRAPH 09. Current scattering models are tuned to the two extremes of thin media and single scattering, or highly scattering materials modeled using the diffusion approximation. The vast intermediate range of materials has no efficient approximation. In this work, we simulate the full space of BSSRDFs, analogous to work on measured BRDF databases. We show new types of scattering behavior, fitting an analytic model and tabulating its parameters. This allows new efficient rendering and reflectance models for a variety of BSSRDFs. Paper: PDF |
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Affine Double and Triple Product Wavelet Integrals for Rendering
ACM Transactions on Graphics 28(2), Article 14, pages 1-17, Apr 2009. Many problems in computer graphics involve integrations of products of functions. Double- and triple-product integrals are commonly used in applications such as all-frequency relighting or importance sampling, but are limited to distant illumination. In contrast, near-field lighting from planar area lights involves an affine transform of the source radiance at different points in space. Our main contribution is a novel affine double- and triple-product integral theory. Paper: PDF Video (AVI 16M) |
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Compressive Light Transport Sensing
ACM Transactions on Graphics 28(1), Article 3, pages 1-18, Jan 2009. In this article we propose a new framework for capturing light transport data of a real scene, based on the recently developed theory of compressive sensing for sparse signals. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting interpixel coherency relations. Additionally, we design new nonadaptive illumination patterns that minimize measurement noise. Paper: PDF |
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Multiscale Texture Synthesis
SIGGRAPH 08, Article 51, pages 1-8. The appearance of many textures changes dramatically with scale; imagine zooming into the planet from outer space to see large scale continent and ocean features, then smaller cities, forests, and finally people and trees. By using an exemplar graph with a few small single-scale exemplars and modifying a standard parallel synthesis method, we develop the first multiscale texture synthesis algorithm. Paper: PDF Video (175M) |
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Light Field Transfer: Global Illumination between Real and Synthetic
Objects
SIGGRAPH 08, Article 57, pages 1-6. By using a light field interface between real and synthetic scenes, we can composite real and virtual objects. Moreover, we can directly simulate multiple bounces of global illumination between them. Our method is suited even for dynamic scenes, and does not require geometric properties or complex image-based appearance capture of the real objects. Paper: PDF Video (55M) |
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A Precomputed Polynomial Representation for Interactive BRDF Editing with
Global Illumination ACM Transactions on Graphics
27(2), Article 13, pages 1--13. Presented at SIGGRAPH 2008. We develop a mathematical framework and practical algorithms to edit BRDFs with global illumination in a complex scene. A key challenge is that light transport for multiple bounces is non-linear in the scene BRDFs. We address this by developing a new bilinear representation of the reflection operator, deriving a polynomial multi-bounce tensor precomputed framework, and reducing the complexity of further bounces. Paper: PDF Video (7M) |
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A Layered, Heterogeneous Reflectance Model for Acquiring and Rendering
Human Skin SIGGRAPH Asia 2008. We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs. We create complex skin visual effects such as veins, tattoos, rashes, and freckles. Paper: PDF (8M) |
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Compressive Structured Light for Recovering Inhomogeneous Participating
Media ECCV 2008. Recovering dynamic inhomogeneous participating media is a significant challenge in vision and graphics. We introduce a new framework of compressive structured light, where patterns are emitted to obtain a line integral of the volume density at each camera pixel. The framework of compressive sensing is then used to recover the density from a sparse set of patterns. Paper: PDF Video (25M) |
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Searching the World's Herbaria: Visual Identification of Plant Species
ECCV 2008. This paper describes our electronic field guide project: a collaboration of researchers in computer vision, mobile computing and botany (the Smithsonian Institution). We have developed a hand-held prototype and recognition algorithms that enable users to take the picture of a leaf and identify the species in the field. The field guide works for Plummer's Island, the woody plants in the DC area, and the trees of NYC's Central Park. Subsequent to this paper, Prof. Belhumeur and collaborators developed and released LeafSnap which is a free iPhone App for visual plant species identification. Paper: PDF Earlier Taxon 2006 paper (PDF) Project Website LeafSnap |
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An Analysis of the BRDF In-Out Factorization for View-Dependent Relighting
EuroGraphics Symposium on Rendering 2008. Interactive rendering with dynamic lighting and changing view is a long standing problem and many recent PRT methods seek to address this by a factorization of the BRDF into incident and outgoing angles. In this paper, we analyze this factorization theoretically using spherical harmonics, and derive the number of terms needed based on the BRDF. One result is that a very large number of terms (10s to 100s) are needed for specular materials. Paper: PDF Video (18M) |
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Large Ray Packets for Real-Time Whitted Ray Tracing
IEEE Symposium on Interactive Ray Tracing 2008. Real-Time Ray Tracing going beyond primary rays and hard shadows, to reflections and refractions, is a long-standing challenge. In this work, we evaluate and develop new algorithms for traversal and frustum culling with large ray packets to get speedups of 3x-6x, enabling real-time Whitted ray tracing on commodity hardware. Paper: PDF |
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A First Order Analysis of Lighting, Shading, and Shadows
ACM Transactions on Graphics, article 2, pages 1-21, Jan 2007. We derive a complete first order or gradient theory of lighting, reflection and shadows, taking both spatial and angular variation of the light field into account. The gradient is by definition a sum of terms, allowing us to consider the relative weight of spatial and angular lighting variation, geometric curvature and bump mapping. Moreover, we derive analytic formulas for the gradients in soft shadow or penumbra regions, demonstrating applications to gradient-based interpolation and sampling. Paper: PDF |
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A Theory of Locally Low Dimensional Light Transport
SIGGRAPH 07, article 62, pages 1-9. We develop a theory of locally low dimensional light transport, to analytically derive the dimensionality of light transport for a local patch. We analyze the eigenvalues for canonical configurations using Szego's eigenvalue theorem. We show mathematically that for symmetric patches of area A, the number of basis functions for glossy reflections increases linearly with A, while for simple cast shadows, it often increases as sqrt(A). There are practical applications to CPCA and other PRT algorithms. Paper: PDF Video (30M) |
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Frequency Domain Normal Map Filtering
SIGGRAPH 07, article 28, pages 1-11. Filtering is critical for representing image-based detail, such as textures or normal maps, across a variety of scales. While mipmapping textures is commonplace, accurate normal map filtering remains a challenging problem because of nonlinearities in shading--we cannot simply average nearby surface normals. In this paper, we show analytically that normal map filtering can be formalized as a spherical convolution of the normal distribution function (NDF) and the BRDF, for a large class of common BRDFs such as Lambertian, microfacet and factored measurements. Our practical algorithms leverage a significant body of previous work that has studied lighting-BRDF convolution. We show how spherical harmonics can be used to filter the NDF for Lambertian and low-frequency specular BRDFs, while spherical von Mises-Fisher distributions can be used for high-frequency materials. Paper: PDF Video (103M) Very Cool Trailer (MOV 54M) |
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A Theory of Frequency Domain Invariants: Spherical Harmonic Identities for BRDF/Lighting Transfer and Image Consistency
ECCV 06, vol IV, pp 41-55, PAMI 30(2), pages 197-213, Feb 2008. We develop new mathematical results based on the spherical harmonic convolution framework for reflection. We derive novel identities, which are the angular frequency domain analogs to common spatial domain invariants such as reflectance ratios. These lead to more general transfer algorithms for inverse rendering, and a novel framework for checking the consistency of images, to detect tampering. Paper: PDF (PAMI 08) PDF (ECCV 06) |
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Dirty Glass: Rendering Contamination on Transparent Surfaces
EuroGraphics Symposium on Rendering, 2007. Real-world transparent objects are seldom clean: Their surfaces have a variety of contaminants such as dust, dirt, and lipids. These contaminations produce a number of complex volumetric scattering effects that must be taken into account when creating realistic renderings. We construct an analytical model for optically thin contaminants, measure the spatially varying thicknesses for a number of glass panes of dust, dirt and lipids, and demonstrate renderings with a variety of volumetric scattering effects. Paper: PDF Video |
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A Real-Time Beam Tracer with Application to Exact Soft Shadows
EuroGraphics Symposium on Rendering, 2007. Beam tracing is one solution to efficiently calculate accurate soft shadows from area light sources. In this paper, we adapt many of the methods for accelerated ray tracing to develop a real-time beam tracer, that is as fast as the best ray tracers for primary rays, and up to 30 times faster for difficult secondary rays, as needed in soft shadows. Moreover, we obtain reference quality exact shadows, without stochastic noise. Paper: PDF Video |
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Time-Varying BRDFs
IEEE Transactions on Visualization and Computer Graphics
13, 3 pages 595-609, 2007. The properties of virtually all real-world materials change with time, causing their BRDFs to be time-varying. In this work, we address the acquisition, analysis, modeling and rendering of a wide range of time-varying BRDFs, including the drying of various types of paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabrics), the accumulation of dusts (household and joint compound) on surfaces, and the melting of materials (chocolate). Analytic BRDF functions are fit to these measurements and the model parameters variations with time are analyzed. Each category exhibits interesting and sometimes non-intuitive parameter trends. These parameter trends are then used to develop analytic time-varying BRDF (TVBRDF) models. Paper: PDF Video (49MB) |
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Viewpoint-Coded Structured Light
CVPR 2007. We introduce a theoretical framework and practical algorithms for replacing time-coded structured light patterns with viewpoint codes, in the form of additional camera locations. Current structured light methods typically use log(N) light patterns, encoded over time, to unambiguously reconstruct N unique depths. We demonstrate that each additional camera location may replace one frame in a temporal binary code. Paper: PDF |
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4D Compression and Relighting with High-Resolution Light Transport Matrices
ACM Symposium on Interactive 3D graphics, 2007, pages 81--88. We use a 4D wavelet transform for relighting with all-frequency illumination. A key observation is that a standard 4D wavelet transform can actually inflate portions of the light transport matrix. Therefore, we present an adaptive 4D wavelet transform that terminates at a level that avoids inflation and maximizes sparsity in the matrix data. Finally, we present an algorithm for fast relighting from adaptively compressed transport matrices. Paper: PDF Video |
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Inverse Shade Trees for Non-Parametric Material Representation and
Editing
SIGGRAPH 06, pages 735-745. We develop an inverse shade tree framework of hierarchical matrix factorizations to provide intuitive, editable representations of high-dimensional measured reflectance datasets of spatially-varying appearance. We introduce a new alternating constrained least squares framework for these decompositions, that preserves the key features of linearity, positivity, sparsity and domain-specific constraints. The SVBRDF is decomposed onto 1D curves and 2D maps, that are easily edited. Paper: PDF Video (24M) |
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Real-Time BRDF Editing in Complex Lighting
SIGGRAPH 06, pages 945-954. In this project, we develop the theory and algorithms to for the first time allow users to edit measured and analytic BRDFs in real time to design materials in their final placement in a scene with complex natural illumination and cast shadows. The system can take as input a variety of analytic and data-driven reflectance models, including the curve-based BRDFs obtained from the inverse shade tree factorization. Paper: PDF (20M) Video (59M) |
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Time-Varying Surface Appearance:
Acquisition, Modeling and Rendering
SIGGRAPH 06, pages 762-771. We conduct the first comprehensive study of time-varying surface appearance, including acquisition of the first database of time-varying processes like burning, drying and decay. We then develop a nonlinear space-time appearance factorization (STAF) that allows easy editing or manipulation such as control, transfer and texture synthesis. We demonstrate a variety of novel time-varying rendering applications using the STAF model. Paper: PDF Video QT (64M) Video AVI (46M) |
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A Compact Factored Representation of Heterogeneous Subsurface Scattering
SIGGRAPH 06, pages 746-753. Heterogeneous subsurface scattering in translucent materials is one of the most beautiful but complex effects. We acquire spatial BSSRDF datasets using a projector, and develop a novel nonlinear factorization that separates a homogeneous kernel, and heterogeneous discontinuities. This enables rendering of complex spatially-varying translucent materials. Paper: PDF (11M) |
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Acquiring Scattering Properties of Participating Media by Dilution
SIGGRAPH 06, pages 1003-1012. We present a simple device and technique for robustly estimating the properties of a broad class of participating media that can be either (a) diluted in water such as juices or beverages, (b) dissolved in water such as powders and sugar/salt crystals, or (c) suspended in water, such as impurities. By diluting in water, we can measure robustly in the single scattering regime. Paper: PDF |
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Reflectance Sharing: Image-Based Rendering from a Sparse Set of Images
PAMI Aug 06, pages 1287-1302 ,
EGSR 05, pages 253-264 We develop the theoretical framework and practical results for image-based rendering of spatially-varying reflectance from a very small number of images. In doing so, we trade off some spatial variation of the reflectance for an increased number of angular samples. The upcoming PAMI paper also includes a novel Fourier analysis of spatial and angular coherence. Paper: PDF Video (83M) EGSR 05 (PDF) |
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Exploiting Temporal Coherence for Incremental All-Frequency Relighting
EGSR 06. ,
Current PRT methods exploit spatial coherence of the lighting (such as with wavelets) and of light transport (such as with CPCA). We consider a significant, yet unexplored form of coherence, temporal coherence of the lighting from frame to frame. We achieve speedups of 3x-4x over conventional PRT with minimal implementation effort, and can trivially be added to almost any existing PRT algorithm. Paper: PDF |
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Efficient Shadows from Sampled Environment Maps
JGT 06 11(1):13-36 There are a number of recent methods to importance sample environment maps. However, these techniques do not exploit the coherence in visibility between nearby rays. We investigate a number of alternatives and develop a simple technique that can speed up the rendering of scenes lit by natural illumination by an order of magnitude with essentially no loss in accuracy. Paper: PDF |
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Modeling Illumination Variation with Spherical Harmonics
Book chapter in Face Processing: Advanced Modeling Methods (pages
385-424, 2006) The appearance of objects including human faces can vary dramatically with the lighting. We present results that use spherical harmonic illumination basis functions to understand this variation for face modeling and recognition, as well as a number of other applications in graphics and vision. Paper: PDF |
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A Practical Analytic Single Scattering Model for Real Time
Rendering Siggraph 05, pages 1040-1049. We present a physically-based model that allows for real-time rendering of a variety of scattering effects like glows around light sources, the effects of scattering on surface shading, and the appearance with complex lighting and BRDFs. The model is based on an analytic integration of the single scattering equations, and can be implemented with simple fragment programs on modern graphics hardware. Paper: PDF Video (74M) |
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Efficiently Combining Positions and Normals for Precise 3D Geometry
Siggraph 05, pages 536-543. We show how depth and normal information, such as from a depth scanner and from photometric stereo, can be efficiently combined to remove the distortions and noise in both, producing very high quality meshes for computer graphics. Paper: PDF |
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Adaptive Numerical Cumulative Distribution Functions for Efficient
Importance Sampling
EGSR 05, pages 11-20 Importance sampling high-dimensional functions like lighting and BRDFs is increasingly important, but a direct tabular representation has storage cost exponential in the number of dimensions. By placing samples non-uniformly, we show that we can develop compact CDFs that enable new applications like sampling from oriented environment maps and multiple importance sampling. Paper: PDF |
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A Signal-Processing Framework for Reflection
ACM Transactions on Graphics (volume 23(4),
Oct 2004, pages 1004-1042) We present a signal-processing framework for analyzing the reflected light field from a homogeneous convex curved surface under distant illumination. This generalizes many of our previous results, showing a unified framework for 2D, 3D lambertian, 3D isotropic and 3D anisotropic cases. Paper: PDF |
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Triple Product Wavelet Integrals for All-Frequency Relighting
Siggraph 04, pages 475-485 We propose a new mathematical and computational analysis of pre-computed light transport. We use factored forms, separately pre-computing the effects of visibility and material properties. Rendering then requires computing triple product integrals at each vertex, involving the lighting, visibility and BRDF. Our main contribution is a general analysis of these triple products likely to have broad applicability in computer graphics and numerical analysis. Paper: PDF (5M) Video (17M) |
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Efficient BRDF Importance Sampling Using a Factored Representation
Siggraph 04, pages 494-503 We introduce a Monte Carlo Importance sampling technique for general analytic and measured BRDFs based on a new BRDF factorization. PDF (8M) |
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A Fourier Theory for Cast Shadows ECCV 04, pages I 146-162 ; PAMI Feb 05, pages 288-295 We show that cast shadows can be mathematically analyzed for many simple configurations, resulting in a standard convolution formula that can be derived analytically in 2D and analyzed numerically in 3D. The results help explain many effects of lighting variability in 3D textures and suggest new bases for that purpose. Paper: ECCV 04 , PAMI 05 |
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Practical Rendering of Multiple Scattering Effects
in Participating Media
EGSR 04 Volumetric light transport effects are significant for many materials like skin, smoke, clouds, snow or water. In particular, one must consider the multiple scattering of light within the volume. We develop a general framework for incorporating analytic point spread functions based on beam spreading, while considering multiple scattering in inhomogeneous media. PDF (2M) |
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Using Specularities for Recognition ICCV 03, pages 1512-1519 We present the first method for using specularities as a positive feature for lighting-insensitive recognition. The method is applied to very difficult objects like shiny crockery and wine glasses. Paper: PDF |
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Spacetime Stereo: A Unifying Framework for Depth from
Triangulation CVPR 03, II-359--II-366 ; PAMI Feb 05, pages 296-302 We propose a common framework, spacetime stereo, which unifies many previous depth from triangulation methods like stereo, laser scanning, and coded structured light. As a practical example, we discuss a new temporal stereo technique for improved shape estimation in static scenes under variable illumination. Paper: CVPR 03 , PAMI 05 |
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All-Frequency Shadows Using Non-Linear Wavelet Lighting Approximation
Siggraph 03, pages 376-381 We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution environment map. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique. PDF (1M) Video (42MB) |
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Structured Importance Sampling of Environment Maps
Siggraph 03, pages 605-612 We introduce structured importance sampling, a new technique for efficiently rendering scenes illuminated by distant natural illumination given in an environment map. PDF Video |
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Analytic PCA Construction for Theoretical Analysis of Lighting Variability, Including Attached Shadows, in a Single Image of a Convex Lambertian Object PAMI Oct 2002,
pp 1322-1333.
We explain for the first time some classic empirical results on lighting variability, and take a first step toward analyzing many classic vision problems under complex lighting. Full Paper: PDF (.8M) |
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Frequency Space Environment Map Rendering:
Siggraph 02, pages 517-526 We present a new method for real-time rendering of objects with complex isotropic BRDFs under distant natural illumination, as specified by an environment map. Our approach is based on spherical frequency space analysis. Full Paper: gzipped PS (4.2M) PDF (3.3M) |
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Analysis of Planar Light Fields From Homogeneous Convex Curved Surfaces Under Distant Illumination
Proceedings of Human Vision and Electronic Imaging VI (part of Photonics West, 2001), pages 185--198
This relatively simple to read paper is the first on the reflection is
convolution idea underlying my PhD thesis, and considers the 2D case using
only Fourier transforms. |
 
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On the relationship between Radiance and Irradiance: Determining the illumination from images of a convex Lambertian object
Journal of the Optical Society of America (JOSA A) Oct 2001, pages 2448-2459
This paper considers the 3D Lambertian case using spherical harmonics
and derives an analytic formula for the irradiance in terms of the
radiance, including the 9 parameter Lambertian BRDF approximation.
One practical application is interactive rendering with An Efficient
Representation for Irradiance Environment Maps.
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A Signal-Processing Framework for Inverse Rendering: Siggraph 01, pages 117-128
This paper is the most mathematical so far and derives the theory for
the general 3D case with arbitrary isotropic BRDFs.
It also applies the
results to the practical problem of inverse rendering under
complex illumination. |
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An Efficient Representation for Irradiance Environment Maps:
Siggraph 01, pages 497-500 We consider the rendering of diffuse objects under distant illumination, as specified by an environment map. Using an analytic expression for the irradiance in terms of spherical harmonic coefficients of the lighting, we show that one needs to compute and use only 9 coefficients, corresponding to the lowest-frequency modes of the illumination, in order to achieve average errors of only 1%. Full Paper: gzipped PS (3.4M) PDF (1M) Talk: PPT (1.8M) Video |
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Efficient Image-Based Methods for
Rendering Soft Shadows:
Siggraph 00, pages 375-384 We present two efficient image-based approaches for computation and display of high-quality soft shadows from area light sources. Our methods are related to shadow maps and provide the associated benefits. Full Paper: gzipped PS (4M) PDF (1.7M) Talk: PPT (1.8M) |
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Creating Generative Models from Range
Images Siggraph 99, pages 195-204 We have explored the creation of high-level parametric models from low-level range data. Our model-based approach is relatively insensitive to noise and missing data and is fairly robust. Full Paper: PS (2.5M) PDF (1.5M) |
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Fast
Construction of Accurate Quaternion Splines:
Siggraph 97, pages 287-292. Dynamic Splines with Constraints for Animation: Caltech CS-TR-97-03. We have explored the use of improved numerical approaches for optimization to automatically create animation from keyframes. The numerical tools developed include adaptive refinement based on the Euler-Lagrange error functional. We have applied this approach to quaternion splines, greatly speeding up a numerical method to construct the optimal rotational curve. Full Paper: Sig 97 PDF Tech Report |
| Teaching (at Berkeley) |
| CS 283 | Advanced Computer Graphics | Spring 2013 | Fall 2010 | Fall 2009 |
| CS 184 | Computer Graphics | Fall 2012 | Spring 2012 | Spring 2010 |
| Teaching (at Columbia) |
| COMS 4160 | Computer Graphics | Fall 2008 | Spring 2008 | Fall 2006 | Fall 2005 | Fall 2004 |
| COMS 4162 | Advanced Computer Graphics | Spring 2006 | Spring 2005 | |||
| COMS 6160 | Topics in Computer Graphics | Visual Appearance (Spr 2007) | Real-Time Rendering (Fall 2004) | Appearance Models (6998 Fall 2002) |
| Students, Alumni and Collaborators |
Group photograph with students, alumni and UCSD graphics faculty at our annual SIGGRAPH 2023 group dinner. Older faculty, student and alumni dinner in 2019, Photograph 1 and Photograph 2
Current PhD Students: Alexandr Kuznetsov, Mohammad Shafiei, Kai-En Lin, Ishit Mehta, Sina Nabizadeh, Bing Xu, Nithin Raghavan, Yash Belhe, Alex Trevithick
Alumni: Tiancheng Sun (PhD 2021, now at Google), Sai Bi (PhD 2021, now at Adobe), Pratul Srinivasan (PhD 2020, now at Google Research), Jiyang Yu (PhD 2020, now at JD AI Research), Lifan Wu (PhD 2020, now at NVIDIA), Zexiang Xu (PhD 2020, now at Adobe), Nima Khademi Kalantari (Postdoc 2016-2018, now at TAMU), Ling-Qi Yan (PhD 2018, now at UCSB), Zak Murez (PhD 2018, now at Magic Leap), Jingwen Wang (MS 2018), Shradha Agarwal (MS 2018), Jean Choi (MS 2018), Ting-Chun Wang (PhD 2017, now at NVIDIA), Weilun Sun (MS 2017), Muhammad Riaz (MS Aug 2016, now at Apple), Michael Tao (PhD Aug 2015, now at Apple), Soham Uday Mehta (PhD May 2015, now at Light), Chi-Wei Tseng (MS thesis Jun 2015, now at Dreamworks), Jong-Chyi Su (MS Jun 2015, now PhD student at UMass), Krishna Mullia (MS Jun 2015, now at Dreamworks), Jiamin Bai (PhD Sep 2014, now at Light), Eno Toeppe (Postdoc 2013-2014, now at Magic Leap), Dikpal Reddy (Postdoc 2011-2013, now at NVIDIA), Brandon Wang (MS,BS 2011-2013, now at Pixar), Milos Hasan (Postdoc 2010-2012, now at AutoDesk), Kevin Egan (PhD Aug 2011, now at DE Shaw), Charles Han (PhD May 2011, now at Google), Manmohan Chandraker (Postdoc 2009-2011, now at UCSD!), Huamin Wang (Postdoc 2009-2011, now at Ohio State), Adrien Bousseau (Postdoc 2009-2010, now at INRIA), Craig Donner (Postdoc 2007-2009, now at Google), Jinwei Gu (PhD May 2010, now at Sarnoff), Fu-Chung Huang (MS May 2010, now at NVIDIA after PhD), Dhruv Mahajan (PhD Aug 2009, now at Microsoft Research), Ryan Overbeck (PhD Aug 2009, now at Google), Bo Sun (PhD Aug 2008, now at Intu Financial), Aner Ben-Artzi (PhD May 2007, now at Sony), Jason Lawrence (Princeton) (PhD June 2006, now faculty at UVA), Simon Premoze (postdoc 2003-2005, now at ILM) , Sebastian Enrique (MS May 2005, now at Electronic Arts), Kalyan Sunkavalli (MS May 2006, now at Adobe after PhD at Harvard), Nandan Dixit (MS Dec 2006, now at Google), Diego Nehab (Princeton) (PhD Jun 2007, now at MSR -> IMPA), Yu-Ting Tseng (MS Dec 2008, now at Google).
| Background |
I joined the Computer Science and
Engineering Department at UC San Diego, starting July 2014.
I was on the faculty of the Electrical Engineering and Computer Science Department at UC Berkeley
from January 2009 to June 2014.
Since the fall of 2002 (until Dec 2008), I was on the faculty of the Columbia Computer Science
Department.
Earlier (1998-2002), I completed my Ph.D in the
Stanford Computer Science
Department , working in the Computer Graphics Laboratory.
Earlier (1994-1998), I was an undergraduate at the California Institute of Technology,
getting a BS, MS in Computer Science and an MS in Physics.
A full CV is also available.
| Personal |
