| Learning 6D object pose estimation using 3D object coordinates E Brachmann, A Krull, F Michel, S Gumhold, J Shotton, C Rother European conference on computer vision, 536-551, 2014 | 591 | 2014 |
| Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image E Brachmann, F Michel, A Krull, M Ying Yang, S Gumhold Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 416 | 2016 |
| DSAC - Differentiable RANSAC for camera localization E Brachmann, A Krull, S Nowozin, J Shotton, F Michel, S Gumhold, ... IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3, 2017 | 395 | 2017 |
| Learning less is more - 6D camera localization via 3D surface regression E Brachmann, C Rother Proc. CVPR 8, 2018 | 251 | 2018 |
| Bop: Benchmark for 6d object pose estimation T Hodan, F Michel, E Brachmann, W Kehl, A GlentBuch, D Kraft, B Drost, ... Proceedings of the European conference on computer vision (ECCV), 19-34, 2018 | 234 | 2018 |
| Learning analysis-by-synthesis for 6D pose estimation in RGB-D images A Krull, E Brachmann, F Michel, M Ying Yang, S Gumhold, C Rother Proceedings of the IEEE International Conference on Computer Vision, 954-962, 2015 | 199 | 2015 |
| Neural-guided RANSAC: Learning where to sample model hypotheses E Brachmann, C Rother Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 141 | 2019 |
| Global hypothesis generation for 6D object pose estimation F Michel, A Kirillov, E Brachmann, A Krull, S Gumhold, B Savchynskyy, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 109 | 2017 |
| BOP challenge 2020 on 6D object localization T Hodaň, M Sundermeyer, B Drost, Y Labbé, E Brachmann, F Michel, ... European Conference on Computer Vision, 577-594, 2020 | 90 | 2020 |
| Random forests versus Neural Networks—What's best for camera localization? D Massiceti, A Krull, E Brachmann, C Rother, PHS Torr 2017 IEEE international conference on robotics and automation (ICRA), 5118-5125, 2017 | 70 | 2017 |
| 6-DOF model based tracking via object coordinate regression A Krull, F Michel, E Brachmann, S Gumhold, S Ihrke, C Rother Asian Conference on Computer Vision, 384-399, 2014 | 62 | 2014 |
| ipose: instance-aware 6d pose estimation of partly occluded objects O Hosseini Jafari, SK Mustikovela, K Pertsch, E Brachmann, C Rother Asian Conference on Computer Vision, 477-492, 2018 | 58* | 2018 |
| Expert sample consensus applied to camera re-localization E Brachmann, C Rother Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 57 | 2019 |
| Poseagent: Budget-constrained 6D object pose estimation via reinforcement learning A Krull, E Brachmann, S Nowozin, F Michel, J Shotton, C Rother Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 43 | 2017 |
| Visual camera re-localization from RGB and RGB-D images using DSAC E Brachmann, C Rother arXiv preprint arXiv:2002.12324, 2020 | 40 | 2020 |
| Reinforced feature points: Optimizing feature detection and description for a high-level task A Bhowmik, S Gumhold, C Rother, E Brachmann Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 37 | 2020 |
| Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. F Michel, A Krull, E Brachmann, MY Yang, S Gumhold, C Rother BMVC, 181.1-181.11, 2015 | 36 | 2015 |
| Consac: Robust multi-model fitting by conditional sample consensus F Kluger, E Brachmann, H Ackermann, C Rother, MY Yang, B Rosenhahn Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 29 | 2020 |
| Carsten Rother E Brachmann, P Kohli, A Blake, S Nowozin Pattern Recognition, 2021 | 18 | 2021 |
| Global hypothesis generation for 6d object pose estimation F Michel, A Kirillov, E Brachmann, A Krull, S Gumhold, B Savchynskyy, ... arXiv preprint arXiv:1612.02287, 2016 | 14 | 2016 |
Carsten RotherProfessor Uni Heidelberg / Germany在 iwr.uni-heidelberg.de 的电子邮件经过验证
