DiffCVGP

References

Here are a few representative papers of the focus areas for this workshop. This list is not intended to be exhaustive, and is only intended as a “reading list” for researchers exploring this exciting line of research. Suggestions and contributions to this list welcome!

  1. Garg R., B.G. V.K., Carneiro G., Reid I. Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue. ECCV 2016.
  2. Zhou T., Brown M., Snavely N., Lowe D. Unsupervised Learning of Depth and Ego-Motion from Video. CVPR 2017.
  3. Godard C., Aodha O., Brostow G. Unsupervised Monocular Depth Estimation with Left-Right Consistency. CVPR 2017.
  4. Godard C., Aodha O., Firman M., Brostow, G. Digging into Self-Supervised Monocular Depth Prediction. ICCV 2019.
  5. Guizilini V., Ambrus R., Pillai S., Raventos A., Gaidon A. PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation. CVPR 2020.
  6. Luo X., Huang, J., Szeliski R., Matzen K., Kopf J. Consistent Video Depth Estimation. SIGGRAPH 2020.
  7. Zhou H., Ummenhofer B., Brox T. DeepTAM: Deep Tracking and Mapping. ECCV 2018.
  8. Jatavallabhula K., Iyer G., Paull L. gradSLAM: Dense SLAM meets automatic differentiation. ICRA 2020.
  9. Loper M., Black M. OpenDR: An Approximate Differentiable Renderer. ECCV 2014.
  10. Kato H., Ushiku Y., Tatsuya H. Neural 3D Mesh Renderer. CVPR 2018.
  11. Liu S., Li T., Chen W., Li H. Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning. ICCV 2019.
  12. Chen W., Gao J., Ling H., Smith E., Lehtinen J., Jacobson A., Fidler S. Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer. Neurips 2019.
  13. Li T., Aittala M., Fredo D., Lehtinen J. Differentiable Monte Carlo Ray Tracing through Edge Sampling. SIGGRAPH Asia 2018.
  14. Nimier-David M., Vicini D., Zeltner T., Jakob W. Mitsuba 2: A Retargetable Forward and Inverse Renderer. SIGGRAPH Asia 2019.
  15. Toussaint M., Allen K., Smith K., Tenenbaum J. Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning. RSS 2018.
  16. Belbute-Peres F., Smith K., Allen K., Tenenbaum J., Kolter Z. End-to-End Differentiable Physics for Learning and Control. Neurips 2018.
  17. Degrave J., Hermans M., Dambre J., Wyffels F. A Differentiable Physics Engine for Deep Learning in Robotics. Frontiers in Neurorobotics 2019.
  18. Hu Y., Liu J., Spielberg A., Tenenbaum J., Freeman W., Wu J., Rus D., and Matusik W. Chainqueen: A real-time differentiable physical simulator for soft robotics. ICRA 2019.
  19. Liang J., Lin M., and Koltun V. Differentiable cloth simulation for inverse problems. Neurips 2019.
  20. Hu Y., Anderson L., Li T., Sun Q., Carr N., Ragan-Kelley J., Durand F.. Difftaichi: Differentiable programming for physical simulation. ICLR 2020.
  21. Wu J., Lu E., Kohli P., Freeman W., Tenenbaum J.. Learning to see physics via visual de-animation. Neurips 2017.
  22. Guen V., Thome N. Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction. CVPR 2020.
  23. Jatavallabhula K., Macklin M., Golemo F., Voleti V., Petrini L., Wiess M., Considine B., Parent-Levesque J., Xie K., Erleben K., Paull L., Shkurti F., Fidler S., Nowrouzezahrai D. gradSim: Differentiable physics and rendering engines for parameter estimation from video.
  24. Jaques M., Burke M., Hospedales T. Physics-as-inverse-graphics:Joint unsupervised learning of objects and physics from video. ICLR 2020.
  25. Jatavallabhula K., Smith E., Lafleche J., Tsang C., Rozantsev A., Chen W., Xiang T., Lebaredian R., Fidler S. Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research. arXiv 2019.
  26. Ravi N., Reizenstein J., Novotny D., Gordon T., Lo W., Johnson J., Gkioxari G. Pytorch3D (https://github.com/facebookresearch/pytorch3d). 2020 (accessed June 15 2020)
  27. Riba E., Mishkin D., Ponsa D., Rublee E., Bradski G. Kornia: an Open Source Differentiable Computer Vision Library for PyTorch. WACV 2020.
  28. Google Research. Tiny Differentiable Simulator (https://github.com/google-research/tiny-differentiable-simulator). 2020 (accessed June 15, 2020).