| Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes Y Xiang, T Schmidt, V Narayanan, D Fox arXiv preprint arXiv:1711.00199, 2017 | 1928 | 2017 |
| Deep local shapes: Learning local sdf priors for detailed 3d reconstruction R Chabra, JE Lenssen, E Ilg, T Schmidt, J Straub, S Lovegrove, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 402 | 2020 |
| Neural 3d video synthesis from multi-view video T Li, M Slavcheva, M Zollhoefer, S Green, C Lassner, C Kim, T Schmidt, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 245 | 2022 |
| Self-supervised visual descriptor learning for dense correspondence T Schmidt, R Newcombe, D Fox IEEE Robotics and Automation Letters 2 (2), 420-427, 2016 | 173 | 2016 |
| DART: Dense Articulated Real-Time Tracking. T Schmidt, RA Newcombe, D Fox Robotics: Science and systems 2 (1), 1-9, 2014 | 155 | 2014 |
| Star: Self-supervised tracking and reconstruction of rigid objects in motion with neural rendering W Yuan, Z Lv, T Schmidt, S Lovegrove Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 79 | 2021 |
| Depth-based tracking with physical constraints for robot manipulation T Schmidt, K Hertkorn, R Newcombe, Z Marton, M Suppa, D Fox 2015 IEEE International Conference on Robotics and Automation (ICRA), 119-126, 2015 | 77 | 2015 |
| Frodo: From detections to 3d objects M Runz, K Li, M Tang, L Ma, C Kong, T Schmidt, I Reid, L Agapito, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 76* | 2020 |
| DART: dense articulated real-time tracking with consumer depth cameras T Schmidt, R Newcombe, D Fox Autonomous Robots 39, 239-258, 2015 | 66 | 2015 |
| Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv 2017 Y Xiang, T Schmidt, V Narayanan, D Fox arXiv preprint arXiv:1711.00199, 0 | 64 | |
| Algorithm-aware neural network based image compression for high-speed imaging R Pinkham, T Schmidt, A Berkovich 2020 IEEE International Conference on Artificial Intelligence and Virtual …, 2020 | 13 | 2020 |
| Dynamic high resolution deformable articulated tracking A Walsman, W Wan, T Schmidt, D Fox 2017 International Conference on 3D Vision (3DV), 38-47, 2017 | 10 | 2017 |
| Neural 3D Video Synthesis Z Lv, M Slavcheva, T Li, M Zollhoefer, SG Green, T Schmidt, M Goesele, ... US Patent App. 17/571,285, 2022 | 4 | 2022 |
| Self-directed lifelong learning for robot vision T Schmidt, D Fox Robotics Research: The 18th International Symposium ISRR, 109-114, 2019 | 4 | 2019 |
| Dynamically programmable image sensor AS Berkovich, R Pinkham, T Schmidt US Patent App. 16/983,863, 2021 | 3 | 2021 |
| Feature query networks: Neural surface description for camera pose refinement H Germain, D DeTone, G Pascoe, T Schmidt, D Novotny, R Newcombe, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 2 | 2022 |
| Identity-disentangled neural deformation model for dynamic meshes B Xu, L Ma, Y Ye, T Schmidt, CD Twigg, S Lovegrove arXiv preprint arXiv:2109.15299, 2021 | 2 | 2021 |
| Explicit Radiance Field Reconstruction from Scratch S Aroudj, M Goesele, RA Newcombe, T Schmidt, FER Ilg, SJ Lovegrove US Patent App. 18/160,937, 2023 | | 2023 |
| ERF: Explicit Radiance Field Reconstruction From Scratch S Aroudj, S Lovegrove, E Ilg, T Schmidt, M Goesele, R Newcombe arXiv preprint arXiv:2203.00051, 2022 | | 2022 |
| Model-Based Self-Supervision for Fine-Grained Image Understanding T Schmidt | | 2019 |
