| 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 | 762 | 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 | 640 | 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 | 325 | 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 | 257 | 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 | 161 | 2019 |
| 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 | 83 | 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 | 82 | 2020 |
| 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 | 78 | 2022 |
| 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 | 76 | 2021 |
| 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 | 66 | 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 | 64 | 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 | 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 | 48 | 2014 |
| 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 | 45 | 2022 |
| Visual odometry learning for unmanned aerial vehicles V Guizilini, F Ramos 2011 IEEE International Conference on Robotics and Automation, 6213-6220, 2011 | 45 | 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 | 41 | 2021 |
| 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 | 40 | 2023 |
| 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 | 40 | 2021 |
| UAV route planning for active disease classification KCT Vivaldini, TH Martinelli, VC Guizilini, JR Souza, MD Oliveira, ... Autonomous robots 43, 1137-1153, 2019 | 36 | 2019 |
| Learning to reconstruct 3D structures for occupancy mapping from depth and color information V Guizilini, F Ramos The International Journal of Robotics Research 37 (13-14), 1595-1609, 2018 | 35 | 2018 |
