| 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 | 3702 | 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 Conference on Computer Vision and Pattern …, 2019 | 124 | 2019 |
| 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 | 52 | 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 | 19 | 2020 |
| 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 | 14 | 2018 |
| 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 | 7 | 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 | 7 | 2020 |
| 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 | 6 | 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 | 4 | 2020 |
| 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 | 3 | 2020 |
| Accelerating Random Kaczmarz Algorithm Based on Clustering Information Y Li, K Mo, H Ye AAAI 2016, 2015 | 3 | 2015 |
| 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, 2020 | 1 | 2020 |
| Where2Act: From Pixels to Actions for Articulated 3D Objects K Mo, L Guibas, M Mukadam, A Gupta, S Tulsiani arXiv preprint arXiv:2101.02692, 2021 | | 2021 |
| Compositionally Generalizable 3D Structure Prediction S Han, J Gu, K Mo, L Yi, S Hu, X Chen, H Su arXiv preprint arXiv:2012.02493, 2020 | | 2020 |
| Rethinking sampling in 3D point cloud generative adversarial networks H Wang, Z Jiang, L Yi, K Mo, H Su, LJ Guibas arXiv preprint arXiv:2006.07029, 2020 | | 2020 |
Leonidas GuibasProfessor of Computer Science, Stanford UniversityVahvistettu sähköpostiosoite verkkotunnuksessa cs.stanford.edu
