David B. Lindell

Assistant Professor
Department of Computer Science
University of Toronto

I’m an Assistant Professor in the Department of Computer Science at the University of Toronto, a faculty affiliate at the Vector Institute, a faculty fellow at AXL, and a founding member of the Toronto Computational Imaging Group.

My research focuses on physically based intelligent sensing: a paradigm that combines physically based models, signal processing, and artificial intelligence to break the limits of current sensing systems and re-think how we reconstruct the world from captured visual information. Along these lines I’ve worked on techniques to image around corners and through scattering media as well as machine learning algorithms to represent, reconstruct, and generate the appearance and geometry of 3D scenes. My work is relevant to a broad range of applications in computational imaging, computer graphics, computer vision, and remote sensing.

news

selected publications

  1. Neural Inverse Rendering from Propagating Light
    Anagh Malik, Benjamin Attal, Andrew Xie, Matthew O’Toole, David B Lindell
    CVPR 2025 (Best Student Paper Award)
    Project Webpage PDF
  2. Opportunistic Single-Photon Time of Flight
    Sotiris Nousias, Mian Wei, Howard Xiao, Maxx Wu, Shahmeer Athar, Kevin J Wang, Anagh Malik, David A Barmherzig, David B Lindell, Kyros Kutulakos
    CVPR 2025 (Oral Presentation)
    Project Webpage PDF
  3. CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models
    Felix Taubner, Ruihang Zhang, Mathieu Tuli, David B Lindell
    CVPR 2025 (Oral Presentation)
    Project Webpage PDF Code
  4. Coherent Optical Modems for Full-Wavefield Lidar
    Parsa Mirdehghan, Brandon Buscaino, Maxx Wu, Doug Charlton, Mohammad E Mousa-Pasandi, Kiriakos N Kutulakos, David B Lindell
    SIGGRAPH Asia 2024
    Project Webpage PDF
  5. Flying With Photons: Rendering Novel Views of Propagating Light
    Anagh Malik, Noah Juravsky, Ryan Po, Gordon Wetzstein, Kiriakos N Kutulakos, David B Lindell
    ECCV 2024 (Oral Presentation)
    Project Webpage PDF Code
  6. 4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
    Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, David B. Lindell
    CVPR 2024
    Project Webpage PDF Code
  7. Passive Ultra-Wideband Single-Photon Imaging
    Mian Wei, Sotiris Nousias, Rahul Gulve, David B. Lindell, Kiriakos N. Kutulakos
    ICCV 2023 (Best Paper/Marr Prize)
    Project Webpage PDF Video
  8. BACON: Band-limited coordinate networks for multiscale scene representation
    David B. Lindell, Dave Van Veen, J. J. Park, Gordon Wetzstein
    CVPR 2022 (Oral Presentation)
    Project Webpage PDF Video Code
  9. AutoInt: Automatic Integration for Fast Neural Volume Rendering
    David B. Lindell*, Julien N. P. Martel*, Gordon Wetzstein
    CVPR 2021
    Project Webpage PDF Video Code
  10. Three-Dimensional Imaging Through Scattering Media based on Confocal Diffuse Tomography
    David B. Lindell, Gordon Wetzstein
    Nature Communications 2020 (2020 Top 50 Physics Articles)
    Project Webpage PDF Video Code
  11. Implicit Neural Representations with Periodic Activation Functions
    Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein
    NeurIPS 2020 (Oral Presentation)
    Project Webpage PDF Video Code
  12. Wave-Based Non-Line-of-Sight Imaging using Fast f–k Migration
    David B. Lindell, Gordon Wetzstein, Matthew O’Toole
    ACM Trans. Graph. (SIGGRAPH) 2019
    Project Webpage PDF Video Code
  13. Confocal Non-Line-of-Sight Imaging Based on the Light Cone Transform
    Matthew O’Toole, David B. Lindell, Gordon Wetzstein
    Nature 2018
    Project Webpage PDF Video Code