| Pointnet++: Deep hierarchical feature learning on point sets in a metric space CR Qi, L Yi, H Su, LJ Guibas Advances in neural information processing systems 30, 2017 | 13382 | 2017 |
| Shapenet: An information-rich 3d model repository AX Chang, T Funkhouser, L Guibas, P Hanrahan, Q Huang, Z Li, ... arXiv preprint arXiv:1512.03012, 2015 | 6190 | 2015 |
| A scalable active framework for region annotation in 3d shape collections L Yi, VG Kim, D Ceylan, IC Shen, M Yan, H Su, C Lu, Q Huang, A Sheffer, ... ACM Transactions on Graphics (ToG) 35 (6), 1-12, 2016 | 1286 | 2016 |
| Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding K Mo, S Zhu, AX Chang, L Yi, S Tripathi, LJ Guibas, H Su Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 786 | 2019 |
| Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation L Yi, H Su, X Guo, LJ Guibas Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 552 | 2017 |
| Sapien: A simulated part-based interactive environment F Xiang, Y Qin, K Mo, Y Xia, H Zhu, F Liu, M Liu, H Jiang, Y Yuan, H Wang, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 490 | 2020 |
| Gspn: Generative shape proposal network for 3d instance segmentation in point cloud L Yi, W Zhao, H Wang, M Sung, LJ Guibas Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 374 | 2019 |
| Structurenet: Hierarchical graph networks for 3d shape generation K Mo, P Guerrero, L Yi, H Su, P Wonka, N Mitra, LJ Guibas arXiv preprint arXiv:1908.00575, 2019 | 328 | 2019 |
| Supervised fitting of geometric primitives to 3d point clouds L Li, M Sung, A Dubrovina, L Yi, LJ Guibas Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 231 | 2019 |
| Category-level articulated object pose estimation X Li, H Wang, L Yi, LJ Guibas, AL Abbott, S Song Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 225 | 2020 |
| Shapenet: An information-rich 3d model repository. arXiv 2015 AX Chang, T Funkhouser, L Guibas, P Hanrahan, Q Huang, Z Li, ... arXiv preprint arXiv:1512.03012, 0 | 201 | |
| Complete & label: A domain adaptation approach to semantic segmentation of lidar point clouds L Yi, B Gong, T Funkhouser Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 168 | 2021 |
| Pointnet++: Deep hierarchical feature learning on point sets in a metric space. arXiv 2017 CR Qi, L Yi, H Su, LJ Guibas arXiv preprint arXiv:1706.02413, 0 | 158 | |
| HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction Y Liu, Y Liu, C Jiang, K Lyu, W Wan, H Shen, B Liang, Z Fu, H Wang, L Yi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 140 | 2022 |
| Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes J Huang, H Zhang, L Yi, T Funkhouser, M Nießner, LJ Guibas Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 135 | 2019 |
| Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining Z Qi, R Dong, G Fan, Z Ge, X Zhang, K Ma, L Yi International Conference on Machine Learning, 28223-28243, 2023 | 121 | 2023 |
| Curriculum deepsdf Y Duan, H Zhu, H Wang, L Yi, R Nevatia, LJ Guibas Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 119 | 2020 |
| Dreamllm: Synergistic multimodal comprehension and creation R Dong, C Han, Y Peng, Z Qi, Z Ge, J Yang, L Zhao, J Sun, H Zhou, H Wei, ... arXiv preprint arXiv:2309.11499, 2023 | 110 | 2023 |
| Deep part induction from articulated object pairs L Yi, H Huang, D Liu, E Kalogerakis, H Su, L Guibas arXiv preprint arXiv:1809.07417, 2018 | 109 | 2018 |
| Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? R Dong, Z Qi, L Zhang, J Zhang, J Sun, Z Ge, L Yi, K Ma arXiv preprint arXiv:2212.08320, 2022 | 95 | 2022 |
Leonidas GuibasProfessor of Computer Science, Stanford University在 cs.stanford.edu 的电子邮件经过验证
