| 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 | 398 | 2020 |
| The strands project: Long-term autonomy in everyday environments N Hawes, C Burbridge, F Jovan, L Kunze, B Lacerda, L Mudrova, J Young, ... IEEE Robotics & Automation Magazine 24 (3), 146-156, 2017 | 209 | 2017 |
| 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 | 187 | 2019 |
| Semantically-guided representation learning for self-supervised monocular depth V Guizilini, R Hou, J Li, R Ambrus, A Gaidon arXiv preprint arXiv:2002.12319, 2020 | 153 | 2020 |
| Is pseudo-lidar needed for monocular 3d object detection? D Park, R Ambrus, V Guizilini, J Li, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 119 | 2021 |
| Meta-rooms: Building and maintaining long term spatial models in a dynamic world R Ambruş, N Bore, J Folkesson, P Jensfelt 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014 | 77 | 2014 |
| Autonomous learning of object models on a mobile robot T Fäulhammer, R Ambruş, C Burbridge, M Zillich, J Folkesson, N Hawes, ... IEEE Robotics and Automation Letters 2 (1), 26-33, 2016 | 71 | 2016 |
| Automatic room segmentation from unstructured 3-D data of indoor environments R Ambruş, S Claici, A Wendt IEEE Robotics and Automation Letters 2 (2), 749-756, 2017 | 68 | 2017 |
| Probabilistic 3d multi-modal, multi-object tracking for autonomous driving H Chiu, J Li, R Ambruş, J Bohg 2021 IEEE International Conference on Robotics and Automation (ICRA), 14227 …, 2021 | 52 | 2021 |
| Modeling motion patterns of dynamic objects by IOHMM Z Wang, R Ambrus, P Jensfelt, J Folkesson 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014 | 44 | 2014 |
| Augmented autonomy: Improving human-robot team performance in urban search and rescue Y Nevatia, T Stoyanov, R Rathnam, M Pfingsthorn, S Markov, R Ambrus, ... 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2008 | 41 | 2008 |
| Where's waldo at time t? using spatio-temporal models for mobile robot search T Krajník, M Kulich, L Mudrová, R Ambrus, T Duckett 2015 IEEE International Conference on Robotics and Automation (ICRA), 2140-2146, 2015 | 38 | 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 | 36 | 2020 |
| Packnet-sfm: 3d packing for self-supervised monocular depth estimation V Guizilini, R Ambrus, S Pillai, A Gaidon arXiv preprint arXiv:1905.02693 5, 1, 2019 | 29 | 2019 |
| Intelligent robotic perception systems C Premebida, R Ambrus, ZC Marton Applications of Mobile Robots, 111-127, 2018 | 29 | 2018 |
| Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario R Ambrus, J Ekekrantz, J Folkesson, P Jensfelt 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 28 | 2015 |
| Semantic labeling of indoor environments from 3d rgb maps M Brucker, M Durner, R Ambruş, ZC Márton, A Wendt, P Jensfelt, ... 2018 IEEE International Conference on Robotics and Automation (ICRA), 1871-1878, 2018 | 26 | 2018 |
| 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 | 25 | 2021 |
| Sparse auxiliary networks for unified monocular depth prediction and completion V Guizilini, R Ambrus, W Burgard, A Gaidon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 25 | 2021 |
| Geometric unsupervised domain adaptation for semantic segmentation V Guizilini, J Li, R Ambruș, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 17 | 2021 |
Adrien GaidonHead of Machine Learning Research, Toyota Research Institute (TRI)Verified email at tri.global
