| Pointnet: Deep learning on point sets for 3d classification and segmentation CR Qi, H Su, K Mo, LJ Guibas Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 16217 | 2017 |
| 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 | 793 | 2019 |
| 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 | 496 | 2020 |
| Structurenet: Hierarchical graph networks for 3d shape generation K Mo, P Guerrero, L Yi, H Su, P Wonka, N Mitra, LJ Guibas Siggraph Asia 2019, 2019 | 329 | 2019 |
| Where2Act: From Pixels to Actions for Articulated 3D Objects K Mo, L Guibas, M Mukadam, A Gupta, S Tulsiani International Conference on Computer Vision (ICCV) 2021, 2021 | 188 | 2021 |
| VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects R Wu, Y Zhao, K Mo, Z Guo, Y Wang, T Wu, Q Fan, X Chen, L Guibas, ... International Conference on Learning Representations (ICLR) 2022, 2021 | 96 | 2021 |
| Generative 3D Part Assembly via Dynamic Graph Learning J Huang, G Zhan, Q Fan, K Mo, L Shao, B Chen, L Guibas, H Dong Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020 | 87 | 2020 |
| GIMO: Gaze-Informed Human Motion Prediction in Context Y Zheng, Y Yang, K Mo, J Li, T Yu, Y Liu, K Liu, LJ Guibas European Conference on Computer Vision (ECCV) 2022, 2022 | 74 | 2022 |
| Learning 3D Part Assembly from a Single Image Y Li, K Mo, L Shao, M Sung, L Guibas European Conference on Computer Vision (ECCV) 2020, 2020 | 71 | 2020 |
| O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning K Mo, Y Qin, F Xiang, H Su, L Guibas Conference on Robot Learning (CoRL) 2021, 2021 | 68 | 2021 |
| AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions Y Wang, R Wu, K Mo, J Ke, Q Fan, L Guibas, H Dong European Conference on Computer Vision (ECCV) 2022, 2022 | 62 | 2022 |
| PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions K Mo, H Wang, X Yan, LJ Guibas European Conference on Computer Vision (ECCV) 2020, 2020 | 48 | 2020 |
| Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories T Luo, K Mo, Z Huang, J Xu, S Hu, L Wang, H Su International Conference on Learning Representations (ICLR) 2020, 2020 | 45 | 2020 |
| StructEdit: Learning structural shape variations K Mo, P Guerrero, L Yi, H Su, P Wonka, NJ Mitra, LJ Guibas Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 42 | 2020 |
| DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation J Yang, K Mo, YK Lai, LJ Guibas, L Gao ACM Transaction on Graphics (ToG), 2020 | 39 | 2020 |
| SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation With Fine-Grained Geometry L Gao, JM Sun, K Mo, YK Lai, LJ Guibas, J Yang IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (7), 8902-8919, 2023 | 32 | 2023 |
| Dsm-net: Disentangled structured mesh net for controllable generation of fine geometry J Yang, K Mo, YK Lai, LJ Guibas, L Gao arXiv preprint arXiv:2008.05440 2 (3), 2020 | 32 | 2020 |
| Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects C Ning, R Wu, H Lu, K Mo, H Dong Advances in Neural Information Processing Systems 36, 4585-4596, 2023 | 30 | 2023 |
| The adobeindoornav dataset: Towards deep reinforcement learning based real-world indoor robot visual navigation K Mo, H Li, Z Lin, JY Lee arXiv preprint arXiv:1802.08824, 2018 | 26 | 2018 |
| Rethinking sampling in 3d point cloud generative adversarial networks H Wang, Z Jiang, L Yi, K Mo, H Su, LJ Guibas CVPR 2021 Workshop "Learning to generate 3D Shapes and Scenes", 2021 | 24 | 2021 |
Leonidas GuibasProfessor of Computer Science, Stanford University在 cs.stanford.edu 的電子郵件地址已通過驗證