| 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 | 11544 | 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 | 5487 | 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 | 1167 | 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 | 689 | 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 | 532 | 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 | 399 | 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 | 350 | 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 | 302 | 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 | 202 | 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 | 184 | 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 | 164 | |
| 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 | 147 | |
| 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 | 145 | 2021 |
| 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 | 125 | 2019 |
| 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 | 104 | 2022 |
| 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 | 103 | 2020 |
| 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 | 96 | 2018 |
| 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 | 72 | 2023 |
| Large-scale 3d shape reconstruction and segmentation from shapenet core55 L Yi, L Shao, M Savva, H Huang, Y Zhou, Q Wang, B Graham, M Engelcke, ... arXiv preprint arXiv:1710.06104, 2017 | 71 | 2017 |
| 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 | 66 | 2022 |
Leonidas GuibasProfessor of Computer Science, Stanford UniversityVahvistettu sähköpostiosoite verkkotunnuksessa cs.stanford.edu
