Manmohan Chandraker

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I am an Assistant 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.




  • 2020: I will serve as Area Chair for AAAI 2021.
  • 2020: I will serve as Area Chair for ECCV 2020.
  • 2020: I will co-organize the CVPR AC Meeting and Workshop at UCSD
  • 2019: Invited talk at ICCV Workshop on Scene Understanding and Autonomous Driving.
  • 2019: Invited talk at ICCV Workshop on Recognition from Low-Quality Images.
  • 2019: Co-organized AutoNUE Workshop on Unconstrained Navigation at ICCV 2019.
  • 2019: Received Google Research Award for work on indoor scenes.
  • 2019: Invited talk at CVPR UG2+ Workshop.
  • 2019: Invited talk at CVPR Workshop on Autonomous Driving.
  • 2019: I will serve as Area Chair for CVPR 2020.
  • 2019: I will serve as Area Chair for ICCV 2019.
  • 2018: Received Google Research Award for work on augmented reality.
  • 2018: Received the NSF CAREER Award, on "Physically-Motivated Learning".
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    Professional Service

    Area Chair
    AAAI 2021, ECCV 2020, CVPR 2020, ICCV 2019, AAAI 2019, ICVGIP 2018, ICCV 2017, ICVGIP 2016, CVPR 2016
    Workshop Organizer
    AC Meeting (CVPR 2020), AC Meeting (CVPR 2019), AutoNUE 2019 (ICCV), AutoNUE 2018 (ECCV)

    Selected Publications | Show All

    My photo  OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets

    Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, Manmohan Chandraker

    Coming soon!      [Project page]

    An open source dataset of indoor scenes with high-quality ground truth for SVBRDF and SV-lighting, along with tools for users to create their own datasets from scans or images.

    My photo  Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
    Kunal Gupta, Manmohan Chandraker
    NeurIPS 2020      [PDF]

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

    My photo  Single-Shot Neural Relighting and SVBRDF Estimation
    Shen Sang, Manmohan Chandraker
    ECCV 2020      [PDF]

    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.

    My photo  Single-View Metrology in the Wild
    Rui Zhu, Xingyi Wang, Yannick Hold-Geoffroy, Federico Perazzi, Jonathan Eisenmann, Kalyan Sunkavalli, Manmohan Chandraker
    ECCV 2020      [PDF]

    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.

    My photo  Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
    Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker
    CVPR 2020 (Oral)      [PDF]

    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.

    My photo  Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
    Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker
    CVPR 2020 (Oral)      [PDF]

    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.

    My photo  Peek-a-Boo: Occlusion Reasoning in Indoor Scenes with Plane Representations
    Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, Manmohan Chandraker
    CVPR 2020 (Oral)      [PDF]

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

    My photo  Universal Semi-Supervised Semantic Segmentation
    T. Kalluri, G. Varma, M.K. Chandraker and C.V. Jawahar
    ICCV 2019      [PDF]

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

    My photo  A Parametric Top-View Representation of Complex Road Scenes
    Z. Wang, B. Liu, S. Schulter and M.K. Chandraker
    CVPR 2019      [PDF]

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

    My photo  Learning Structure-And-Motion-Aware Rolling Shutter Correction
    B. Zhuang, Q.-H. Tran, P. Ji, L.F. Cheong and M.K. Chandraker
    CVPR 2019 (Oral)      [PDF]

    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.

    My photo  Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
    L. Tran, K. Sohn, X. Yu, X. Liu, and M.K. Chandraker
    CVPR 2019      [PDF] [Supplementary]

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

    My photo  Learning to Simulate
    N. Ruiz, S. Schulter and M.K. Chandraker
    ICLR 2019      [PDF]

    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.

    My photo  Unsupervised Domain Adaptation for Distance Metric Learning
    K. Sohn, W. Shang, X. Yu and M.K. Chandraker
    ICLR 2019      [PDF]

    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.

    My photo  Single-Shot Analysis of Refractive Shapes Using Convolutional Neural Networks
    J. Stets, Z. Li, J. Frisvad and M.K. Chandraker
    WACV 2019      [PDF]

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

    My photo  AutoNUE: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
    G. Varma, A. Subramanian, A. Namboodiri, M.K. Chandraker and C.V. Jawahar
    WACV 2019      [PDF]

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

    My photo  Learning to Reconstruct Shape and Spatially-Varying Reflectance with a Single Image
    Z. Li, Z. Xu, R. Ramamoorthi, K. Sunkavalli and M.K. Chandraker
    SIGGRAPH Asia 2018      [PDF]

    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.

    My photo  Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
    Z. Li, K. Sunkavalli and M.K. Chandraker
    ECCV 2018 (Oral)      [PDF]

    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.

    My photo  Learning to Look around Objects for Top-View Representations of Outdoor Scenes
    S. Schulter, M. Zhai, N. Jacobs and M.K. Chandraker
    ECCV 2018      [PDF]

    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.

    My photo  Learning to Adapt Structured Output Space for Semantic Segmentation
    Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang and M.K. Chandraker
    CVPR 2018 (Spotlight)      [PDF]

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

    My photo  Learning to See through Turbulent Water
    Z. Li, Z. Murez, D. Kriegman, R. Ramamoorthi and M.K. Chandraker
    WACV 2018      [PDF]

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

    My photo  Learning Efficient Object Detection Models with Knowledge Distillation
    G. Chen, W. Choi, X. Yu, T. Han and M.K. Chandraker
    NeurIPS 2017      [PDF]

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

    My photo  Robust Energy Minimization for BRDF-Invariant Shape from Light Fields
    Z. Li, Z. Xu, R. Ramamoorthi and M.K. Chandraker
    CVPR 2017      [PDF]

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

    My photo  Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
    C. Li, Z. Zia, Q.-H. Tran, X. Yu, G. Hager and M.K. Chandraker
    CVPR 2017      [PDF]

    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.

    My photo  DESIRE: Distant Future Prediction in Dynamic Scenes with Multiple Interacting Agents
    N. Lee, W. Choi, P. Vernaza, C. Choy, P. Torr and M.K. Chandraker
    CVPR 2017      [PDF]

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

    My photo  Universal Correspondence Network
    C. Choy, J. Gwak, S. Savarese and M.K. Chandraker
    NeurIPS 2016 (Oral)      [PDF]

    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.

    My photo  WarpNet: Weakly Supervised Matching for Single-View Reconstruction
    A. Kanazawa, D. Jacobs and M.K. Chandraker
    CVPR 2016      [PDF]

    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.

    My photo  SVBRDF-Invariant Shape and Reflectance Recovery from Light Fields
    T.-C. Wang, M.K. Chandraker, A. Efros and R. Ramamoorthi
    CVPR 2016 (Oral)      [PDF]

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

    My photo  The Information Available to a Moving Observer on Shape with Unknown, Isotropic BRDFs
    M.K. Chandraker
    IEEE PAMI 2015 [Spl. Issue, Best of CVPR 2014]      [PDF]

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

    My photo  Joint SFM and Detection Cues in 3D Object Localization for Autonomous Driving
    S. Song and M.K. Chandraker
    CVPR 2015 (Oral)      [PDF]

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

    My photo  What Camera Motion Reveals About Shape with Unknown BRDF
    M.K. Chandraker
    CVPR 2014 (Oral) [Best Paper Award]      [PDF] [Tech Report]

    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.

    My photo  What Motion Reveals About Shape with Unknown BRDF and Lighting
    M.K. Chandraker, D. Reddy, Y. Wang and R. Ramamoorthi
    CVPR 2013 (Oral)      [PDF]

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

    My photo  Dense Object Reconstruction with Semantic Priors
    Y. Bao, M.K. Chandraker, Y. Lin and S. Savarese
    CVPR 2013 (Oral)      [PDF]

    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.

    My photo  On Differential Photometric Reconstruction for Unknown, Isotropic BRDFs
    M.K. Chandraker, J. Bai and R. Ramamoorthi
    IEEE PAMI 2013 [Spl. Issue, Best of CVPR 2011]      [PDF]

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

    My photo  What An Image Reveals About Material Reflectance
    M.K. Chandraker and R. Ramamoorthi
    ICCV 2011 (Oral)      [PDF]

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

    My photo  A Theory of Photometric Reconstruction for Unknown Isotropic Reflectances
    M.K. Chandraker, J. Bai and R. Ramamoorthi
    CVPR 2011 (Oral)      [PDF]

    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.

    My photo  Globally Optimal Algorithms for Stratified Autocalibration
    M.K. Chandraker, S. Agarwal, D.J. Kriegman and S. Belongie
    IJCV 2009      [PDF]

    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.

    My photo  Globally Optimal Bilinear Programming for Computer Vision Applications
    M.K. Chandraker and D.J. Kriegman
    CVPR 2008 (Oral)      [PDF] [Code]

    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.

    My photo  Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
    M.K. Chandraker, S. Agarwal, D.J. Kriegman and S. Belongie
    ICCV 2007 (Oral) [Marr Prize Honorable Mention]      [PDF] [Project Site]

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

    My photo  ShadowCuts: Photometric Stereo with Shadows
    M.K. Chandraker, S. Agarwal and D.J. Kriegman
    CVPR 2007      [PDF] [Data+Code]

    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.

    My photo  Practical Global Optimization for Multiview Geometry
    S. Agarwal, M.K. Chandraker, F. Kahl, D.J. Kriegman and S. Belongie
    ECCV 2006 (Oral)      [PDF] [Code]

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

    My photo  Reflections on the Generalized Bas-Relief Ambiguity
    M.K. Chandraker, F. Kahl and D.J. Kriegman
    CVPR 2005 (Oral)      [PDF]

    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.

    Short bio

    I am an assistant professor at the CSE department of the University of California, San Diego. My research interests are in computer vision, machine learning and graphics-based vision, with applications to autonomous driving and augmented reality. My works have received the Marr Prize Honorable Mention for Best Paper at ICCV 2007, the 2009 CSE Dissertation Award for Best Thesis at UCSD, a PAMI special issue on best papers of CVPR 2011, the Best Paper Award at CVPR 2014, the 2018 NSF CAREER Award and the 2018 and 2019 Google Daydream Research Award. I serve as an Area Chair at CVPR, ICCV, ECCV, ICVGIP and AAAI.