no code implementations • 28 Jun 2022 • Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula, Jeannette Bohg
In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios.
no code implementations • 20 Aug 2021 • Kaustubh Mani, N. Sai Shankar, Krishna Murthy Jatavallabhula, K. Madhava Krishna
Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view.
no code implementations • ICLR 2021 • Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler
We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.
1 code implementation • 25 Nov 2020 • Rahul Sajnani, AadilMehdi Sanchawala, Krishna Murthy Jatavallabhula, Srinath Sridhar, K. Madhava Krishna
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images.
1 code implementation • 23 Nov 2020 • Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang, Ye Wang, Pradeep Kumar Jayaraman, Krishna Murthy Jatavallabhula
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
2 code implementations • 19 Feb 2020 • Kaustubh Mani, Swapnil Daga, Shubhika Garg, N. Sai Shankar, Krishna Murthy Jatavallabhula, K. Madhava Krishna
We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image.
no code implementations • 10 Feb 2020 • Gokul B. Nair, Swapnil Daga, Rahul Sajnani, Anirudha Ramesh, Junaid Ahmed Ansari, Krishna Murthy Jatavallabhula, K. Madhava Krishna
In this paper, we tackle the problem of multibody SLAM from a monocular camera.
6 code implementations • 12 Nov 2019 • Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research.
1 code implementation • 23 Oct 2019 • Krishna Murthy Jatavallabhula, Soroush Saryazdi, Ganesh Iyer, Liam Paull
Blending representation learning approaches with simultaneous localization and mapping (SLAM) systems is an open question, because of their highly modular and complex nature.
Representation Learning
Simultaneous Localization and Mapping