| Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data X Chen, S Li, B Mersch, L Wiesmann, J Gall, J Behley, C Stachniss IEEE Robotics and Automation Letters 6 (4), 6529-6536, 2021 | 163 | 2021 |
| KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way I Vizzo, T Guadagnino, B Mersch, L Wiesmann, J Behley, C Stachniss IEEE Robotics and Automation Letters 8 (2), 1029-1036, 2023 | 154 | 2023 |
| Automatic labeling to generate training data for online LiDAR-based moving object segmentation X Chen, B Mersch, L Nunes, R Marcuzzi, I Vizzo, J Behley, C Stachniss IEEE Robotics and Automation Letters 7 (3), 6107-6114, 2022 | 65 | 2022 |
| Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions B Mersch, X Chen, I Vizzo, L Nunes, J Behley, C Stachniss IEEE Robotics and Automation Letters 7 (3), 7503-7510, 2022 | 46 | 2022 |
| Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks B Mersch, X Chen, J Behley, C Stachniss Conference on Robot Learning (CoRL), 2021 | 45 | 2021 |
| Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks B Mersch, T Höllen, K Zhao, C Stachniss, R Roscher 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 36 | 2021 |
| ERASOR2: Instance-aware robust 3D mapping of the static world in dynamic scenes H Lim, L Nunes, B Mersch, X Chen, J Behley, H Myung, C Stachniss Robotics: Science and Systems (RSS 2023), 2023 | 14 | 2023 |
| Building volumetric beliefs for dynamic environments exploiting map-based moving object segmentation B Mersch, T Guadagnino, X Chen, I Vizzo, J Behley, C Stachniss IEEE Robotics and Automation Letters, 2023 | 13 | 2023 |
| Make it dense: Self-supervised geometric scan completion of sparse 3d lidar scans in large outdoor environments I Vizzo, B Mersch, R Marcuzzi, L Wiesmann, J Behley, C Stachniss IEEE Robotics and Automation Letters 7 (3), 8534-8541, 2022 | 9 | 2022 |
| Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition I Vizzo, B Mersch, L Nunes, L Wiesmann, T Guadagnino, C Stachniss | 3* | |
| Effectively Detecting Loop Closures using Point Cloud Density Maps S Gupta, T Guadagnino, B Mersch, I Vizzo, C Stachniss Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024 | 2 | 2024 |
| Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds M Zeller, VS Sandhu, B Mersch, J Behley, M Heidingsfeld, C Stachniss IEEE Transactions on Robotics, 2023 | 2 | 2023 |
| Radar velocity transformer: Single-scan moving object segmentation in noisy radar point clouds M Zeller, VS Sandhu, B Mersch, J Behley, M Heidingsfeld, C Stachniss 2023 IEEE International Conference on Robotics and Automation (ICRA), 7054-7061, 2023 | 2 | 2023 |
| Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization I Hroob, B Mersch, C Stachniss, M Hanheide IEEE Robotics and Automation Letters, 2024 | | 2024 |
| Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion L Nunes, R Marcuzzi, B Mersch, J Behley, C Stachniss Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | | 2024 |
Cyrill StachnissProfessor for Photogrammetry & Robotics, University of Bonn在 igg.uni-bonn.de 的电子邮件经过验证
