| PointRCNN: 3d object proposal generation and detection from point cloud S Shi, X Wang, H Li The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 …, 2019 | 611 | 2019 |
| PV-RCNN: Point-voxel feature set abstraction for 3d object detection S Shi, C Guo, L Jiang, Z Wang, J Shi, X Wang, H Li The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 …, 2020 | 226 | 2020 |
| FP-DNN: An automated framework for mapping deep neural networks onto FPGAs with RTL-HLS hybrid templates Y Guan, H Liang, N Xu, W Wang, S Shi, X Chen, G Sun, W Zhang, J Cong 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom …, 2017 | 207 | 2017 |
| From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network S Shi, Z Wang, J Shi, X Wang, H Li IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020, 2020 | 171* | 2020 |
| Gal: Geometric adversarial loss for single-view 3d-object reconstruction L Jiang, S Shi, X Qi, J Jia Proceedings of the European conference on computer vision (ECCV), 802-816, 2018 | 74 | 2018 |
| PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation L Jiang, H Zhao, S Shi, S Liu, CW Fu, J Jia The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 …, 2020 | 58 | 2020 |
| Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection J Deng, S Shi, P Li, W Zhou, Y Zhang, H Li AAAI 2021, 2020 | 12 | 2020 |
| SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud H Yi, S Shi, M Ding, J Sun, K Xu, H Zhou, Z Wang, S Li, G Wang 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, 2020 | 11 | 2020 |
| PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection S Shi, L Jiang, J Deng, Z Wang, C Guo, J Shi, X Wang, H Li arXiv preprint arXiv:2102.00463, 2021 | 9 | 2021 |
| OpenPCDet: An open-source toolbox for 3d object detection from point clouds OD Team https://github.com/open-mmlab/OpenPCDet, 2020 | 7 | 2020 |
| Feature Intertwiner for Object Detection H Li, B Dai, S Shi, W Ouyang, X Wang International Conference on Learning Representation (ICLR), 2019, 2019 | 7 | 2019 |
| ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection J Yang*, S Shi*, Z Wang, H Li, X Qi CVPR 2021, 2021 | 4 | 2021 |
| Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds B Cheng, L Sheng, S Shi, M Yang, D Xu CVPR 2021, 2021 | 2 | 2021 |
| PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection/3D Tracking/Domain Adaptation of Waymo Open Dataset Challenges S Shi, C Guo, J Yang, H Li Technical report for Waymo Open Dataset Challenges at Workshop of CVPR 2020 …, 2020 | 2 | 2020 |
| LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector X Guo, S Shi, X Wang, H Li arXiv preprint arXiv:2108.08258, 2021 | | 2021 |
| ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection J Yang, S Shi, Z Wang, H Li, X Qi arXiv preprint arXiv:2108.06682, 2021 | | 2021 |
| Supplementary Material for ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection J Yang, S Shi, Z Wang, H Li, X Qi | | |
Hongsheng Li (李鸿升)Assistant Professor at The Chinese University of Hong Kong在 ee.cuhk.edu.hk 的电子邮件经过验证
