| 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 | 847 | 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 | 372 | 2021 |
| 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 | 295 | 2021 |
| 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 | 278 | 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 | 268 | 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 | 259 | 2019 |
| Simple-bev: What really matters for multi-sensor bev perception? AW Harley, Z Fang, J Li, R Ambrus, K Fragkiadaki 2023 IEEE International Conference on Robotics and Automation (ICRA), 2759-2765, 2023 | 123 | 2023 |
| Multi-frame self-supervised depth with transformers V Guizilini, R Ambruș, D Chen, S Zakharov, A Gaidon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 102 | 2022 |
| 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 | 99 | 2014 |
| 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 | 93 | 2017 |
| 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 | 83 | 2021 |
| 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 | 75 | 2016 |
| 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 | 72 | 2020 |
| Towards zero-shot scale-aware monocular depth estimation V Guizilini, I Vasiljevic, D Chen, R Ambruș, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 69 | 2023 |
| Shapo: Implicit representations for multi-object shape, appearance, and pose optimization MZ Irshad, S Zakharov, R Ambrus, T Kollar, Z Kira, A Gaidon European Conference on Computer Vision, 275-292, 2022 | 67 | 2022 |
| 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 | 60 | 2014 |
| Full surround monodepth from multiple cameras V Guizilini, I Vasiljevic, R Ambrus, G Shakhnarovich, A Gaidon IEEE Robotics and Automation Letters 7 (2), 5397-5404, 2022 | 57 | 2022 |
| Intelligent robotic perception systems C Premebida, R Ambrus, ZC Marton Applications of mobile robots, 111-127, 2018 | 56 | 2018 |
| Learning optical flow, depth, and scene flow without real-world labels V Guizilini, KH Lee, R Ambruş, A Gaidon IEEE Robotics and Automation Letters 7 (2), 3491-3498, 2022 | 55 | 2022 |
| 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 | 55 | 2015 |
Adrien GaidonAdjunct Professor, StanfordVerified email at stanford.edu
