| 3d packing for self-supervised monocular depth estimation V Guizilini, R Ambrus, S Pillai, A Raventos, A Gaidon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 515 | 2020 |
| Bitcoin Transaction Graph Analysis M Fleder, M Kester, S Pillai | 292 | 2013 |
| Superdepth: Self-supervised, super-resolved monocular depth estimation S Pillai, R Ambruş, A Gaidon 2019 International Conference on Robotics and Automation (ICRA), 9250-9256, 2019 | 210 | 2019 |
| Monocular SLAM Supported Object Recognition S Pillai, J Leonard Robotics: Science and Systems XI, 2015 | 169 | 2015 |
| Learning Articulated Motions From Visual Demonstration S Pillai, M Walter, S Teller Proceedings of Robotics: Science and Systems 10, 2014 | 60 | 2014 |
| Towards visual ego-motion learning in robots S Pillai, JJ Leonard Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, 2017 | 48 | 2017 |
| High-Performance and Tunable Stereo Reconstruction S Pillai, S Ramalingam, JJ Leonard 2016 IEEE International Conference on Robotics and Automation (ICRA), 2015 | 46 | 2015 |
| Robust semi-supervised monocular depth estimation with reprojected distances V Guizilini, J Li, R Ambrus, S Pillai, A Gaidon Conference on robot learning, 503-512, 2020 | 45 | 2020 |
| Line-sweep: Cross-ratio for wide-baseline matching and 3d reconstruction S Ramalingam, M Antunes, D Snow, G Hee Lee, S Pillai Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 38 | 2015 |
| Self-supervised 3d keypoint learning for ego-motion estimation J Tang, R Ambrus, V Guizilini, S Pillai, H Kim, P Jensfelt, A Gaidon Conference on Robot Learning, 2085-2103, 2021 | 27 | 2021 |
| Neural outlier rejection for self-supervised keypoint learning J Tang, H Kim, V Guizilini, S Pillai, R Ambrus arXiv preprint arXiv:1912.10615, 2019 | 25 | 2019 |
| Neural ray surfaces for self-supervised learning of depth and ego-motion I Vasiljevic, V Guizilini, R Ambrus, S Pillai, W Burgard, G Shakhnarovich, ... 2020 International Conference on 3D Vision (3DV), 1-11, 2020 | 19 | 2020 |
| A Summary of Team MIT’s Approach to the Virtual Robotics Challenge R Tedrake, M Fallon, S Karumanchi, S Kuindersma, M Antone, ... IEEE International Conference on Robotics and Automation (ICRA), 2014 | 18 | 2014 |
| Exploring big volume sensor data with Vroom O Moll, A Zalewski, S Pillai, S Madden, M Stonebraker, V Gadepally | 17 | 2017 |
| Two stream networks for self-supervised ego-motion estimation R Ambrus, V Guizilini, J Li, S Pillai, A Gaidon Conference on Robot Learning, 1052-1061, 2020 | 15 | 2020 |
| Pillarflow: End-to-end birds-eye-view flow estimation for autonomous driving KH Lee, M Kliemann, A Gaidon, J Li, C Fang, S Pillai, W Burgard 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 14 | 2020 |
| Systems and methods for semi-supervised training using reprojected distance loss V Guizilini, S Pillai, RA Ambrus, J Li, AD Gaidon US Patent 11,138,751, 2021 | 13 | 2021 |
| SLAMinDB: Centralized graph databases for mobile robotics D Fourie, S Classens, S Pillai, R Mata, J Leonard IEEE International Conference on Robotics and Automation (ICRA), 2017, 2017 | 13 | 2017 |
| Finding label and model errors in perception data with learned observation assertions D Kang, N Arechiga, S Pillai, PD Bailis, M Zaharia Proceedings of the 2022 International Conference on Management of Data, 496-505, 2022 | 11 | 2022 |
| High-speed and tunable scene reconstruction systems and methods using stereo imagery JJ Leonard, S Pillai US Patent 10,477,178, 2019 | 10 | 2019 |
Adrien GaidonHead of Machine Learning Research, Toyota Research Institute (TRI)Verified email at tri.global
