| 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 | 799 | 2020 |
| Detr3d: 3d object detection from multi-view images via 3d-to-2d queries Y Wang, VC Guizilini, T Zhang, Y Wang, H Zhao, J Solomon Conference on Robot Learning, 180-191, 2022 | 709 | 2022 |
| 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 | 345 | 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 | 269 | 2020 |
| MEMS-based IMU drift minimization: Sage Husa adaptive robust Kalman filtering M Narasimhappa, AD Mahindrakar, VC Guizilini, MH Terra, SL Sabat IEEE Sensors Journal 20 (1), 250-260, 2019 | 172 | 2019 |
| 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 | 89 | 2022 |
| The impact of DoS attacks on the AR. Drone 2.0 G Vasconcelos, G Carrijo, R Miani, J Souza, V Guizilini 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics …, 2016 | 86 | 2016 |
| Real-time panoptic segmentation from dense detections R Hou, J Li, A Bhargava, A Raventos, V Guizilini, C Fang, J Lynch, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 85 | 2020 |
| 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 | 80 | 2021 |
| Droid: A large-scale in-the-wild robot manipulation dataset A Khazatsky, K Pertsch, S Nair, A Balakrishna, S Dasari, S Karamcheti, ... arXiv preprint arXiv:2403.12945, 2024 | 79 | 2024 |
| 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 | 68 | 2020 |
| Failure detection in row crops from UAV images using morphological operators HC Oliveira, VC Guizilini, IP Nunes, JR Souza IEEE Geoscience and Remote Sensing Letters 15 (7), 991-995, 2018 | 63 | 2018 |
| 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 | 55 | 2022 |
| 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 | 52 | 2023 |
| 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 | 49 | 2022 |
| Online self-supervised multi-instance segmentation of dynamic objects A Bewley, V Guizilini, F Ramos, B Upcroft 2014 IEEE International Conference on Robotics and Automation (ICRA), 1296-1303, 2014 | 47 | 2014 |
| Visual odometry learning for unmanned aerial vehicles V Guizilini, F Ramos 2011 IEEE International Conference on Robotics and Automation, 6213-6220, 2011 | 46 | 2011 |
| Marionette: Self-supervised sprite learning D Smirnov, M Gharbi, M Fisher, V Guizilini, A Efros, JM Solomon Advances in Neural Information Processing Systems 34, 5494-5505, 2021 | 44 | 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 | 42 | 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 | 37 | 2019 |
