Manmohan Chandraker

I am an Associate Professor at the CSE Department of UCSD. I work on computer vision, in particular 3D reconstruction, scene understanding and graphics-based vision, with applications in autonomous driving, robotics and augmented reality.

• Email: mkchandraker [AT] eng [DOT] ucsd [ANOTHER_DOT] edu

• Office: Room 4122, EBU-3B, UC San Diego

Affiliations:

• Visual Computing Center
• Contextual Robotics Institute
• Halicioglu Data Science Institute

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ECCV 2022 (Oral)      [PDF] [Project page] [Code] [Video]

Estimation of visible and invisible light sources from a single image of an indoor scene, to enable scene relighting and object insertion with full global effects.

ECCV 2022      [PDF] [Project page] [Code]

Unsupervised domain adaptation that scales to a large number of categories using a memory bank.

ECCV 2022 (Oral)      [PDF] [Project page] [Code] [Video]

A level-set theory bridge between explicit and implicit shape representations that allows user-defined editing of neural implicit geometry.

CVPR 2022 (Oral)      [PDF] [Project page] [Code] [Video]

A vision transformer architecture for inverse rendering in indoor scenes that allows learning long-range global interactions.

CVPR 2022      [PDF] [Project page] [Code] [Video]

A framework that uses a set of images of an indoor scene to create a photorealistic digital twin with high-quality material and lighting.

CVPR 2021 (Oral)      [PDF] [Project page] [Code] [Video]

An open source dataset of indoor scenes with high-quality tools and ground truth for shape, material and lighting, for augmented reality and robotics applications.

Sriram Narayanan, Ramin Moslemi, Francesco Pittaluga, Buyu Liu, Manmohan Chandraker

A multi-choice learning objective for diverse future trajectory prediction and a lane anchor network for 3D scene-consistent outputs.

Astuti Sharma, Tarun Kalluri, Manmohan Chandraker

An instance-affinity based criterion with a multi-sample contrastive loss for improved domain alignment.

Neural ODEs to generate 3D meshes with guaranteed manifoldness, enabling physically meaningful applications like rendering, simulations and 3D printing.

A physically-motivated network for joint shape and material estimation, as well as relighting under novel illumination conditions, using a single image captured by a mobile phone camera.

Constant-time, scene consistent and diverse trajectory prediction regardless of number of agents, along with a dynamics simulator that diversifies top-views of real scenes.

Recover object height and camera parameters from a single unconstrained image, through a network that imbibes weakly supervised geometric constraints through bounding boxes and the horizon.

A physically-motivated network that models refractions and reflections to recover high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images.

A physically-motivated network that recovers shape, complex material and spatially varying lighting from a single mobile phone image to enable photorealistic indoor AR applications.

A novel planar representation for indoor 3D scene understanding that reasons about occlusions, with new metrics and a new dataset.

Learning fair representations that perform well across both data-rich and data-poor domains, applied to semantic segmentation that minimizes annotation and deployment costs.

A parametric representation for 3D scene understanding of complex road scenes that is intuitive for human visualization and interpretable for higher-level decision making.

Theoretical limits on SFM with a rolling-shutter camera and leveraging data-driven priors through a network that learns camera motion and scene stucture to undistort a single rolling shutter image.

Unsupervised domain adaptation that combines insights from semi-supervised learning for feature-level adaptation and 3D geometry-guided image synthesis for pixel-level adaptation.

A reinforcement learning-based method for automatically adjusting the parameters of any non-differentiable simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data.

Making face recognition fair across ethnicities through unsupervised adaptation across domains with non-overlapping label spaces, retaining identification power on all ethnicities while keeping representations for all identities well-separated.

Single-image depth map and normal estimation for transparent shapes using a network trained on a new synthetic dataset.

Taking a step towards self-driving on Indian roads through a novel large-scale dataset that provides instance segmentation and object detection labels.

A differential rendering layer with global illumination to train a network that recovers shape and complex, spatially-varying material using a single image acquired with a mobile phone camera.

A differentiable rendering layer that models image formation with a complex spatially-varying BRDF to recover high-quality material information using a single mobile phone image.

Predict occluded portions of the scene layout by looking around foreground objects like cars or pedestrians, with learned priors and rules about typical road layouts from simulated or, if available, map data.

Unsupervised domain adaptation for semantic segmentation that aligns spatial similarities across domains through a structured output space.

Learning appearance and geometric priors for single-image undistortion of an image observed through a dynamic refractive medium such as water waves.

Fast and accurate object detection network through knowledge distillation that transfers insights to a compact student model from a higher-capacity teacher model.

A variational energy minimization framework for robust recovery of shape in multiview stereo with unknown BRDF, with applications to light field cameras.

Deep supervision to sequentially infer intermediate concepts associated with the final task allows better generalization, demonstrated with an application to 3D semantic parsing from 2D images.

Predicting future trajectories in complex 3D scenes by accounting for the multimodaility of future behaviors, scene semantics and interactions among objects.

Deep metric learning to learn a feature space for geometric and semantic correspondence, with novel architectures such as a convolutional spatial transformer to handle local variations.

Single-view reconstruction of non-rigid objects like birds without using part annotations, through a deformation-aware synthetic data generation and spatial priors in a deep network.

Shape and spatially-varying material recovery from a single light field image of an object, using theoretical invariants from differential stereo with general reflectance.

Theoretical inavriants that eliminate BRDF from a system of differential stereo equations to yield PDE constraints that delineate the extent of shape recovery.

Single-camera 3D localization of objects in traffic scenes by combining cues from SFM point tracks and object detection bounding boxes.

Specification of the extent of shape recovery through differential stereo invariants obtained using images of an object with unknown material observed under a small camera motion.

Theoretical analysis of shape recovery using differential flow invariants obtained under small motions of an object relative to its environment.

Semantic 3D reconstruction using shape priors consisting of a mean shape for the commonality of shapes across a category and weighted anchor points to encode similarities between instances in the form of appearance and spatial consistency.

A photometric flow relation that specifies theoretical extent of shape recovery possible with differential motion of a light source.

A semiparametric regression for single-image material estimation that achieves better generalization and interpretability, enabling applications such as relighting and material editing.

Differential invariants under light source motion that determine the extent of shape recovery and prior information needed for it, using images of an object with unknown material.

Tight convex relations in a branch and bound framework for globally optimal estimation of the plane at infinity with chirality constraints and dual image of the absolute conic with semidefinite constraints, allowing metric upgrade of a projective reconsturction.

Globally optimal solutions to bilinear problems through convex relations in a branch and bound framework, demonstrated for face 3D morphable model fitting and non-rigid SFM.

Globally optimal metric upgrade of a projective reconstruction, through efficient convex relaxations for estimating the plane at infinity and camera intrinsic parameters.

An algorithm for performing Lambertian photometric stereo in the presence of shadows. It reduces the low frequency bias inherent to the normal integration process and ensures that the recovered surface is consistent with the shadowing configuration.

A practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography estimation.

For general nonconvex surfaces, interreflections completely resolve the GBR ambiguity. The full Euclidean geometry can be recovered from uncalibrated photometric stereo for which the light source directions and strengths are unknown.