Kaichun Mo
Kaichun Mo
Ph.D. Student, Stanford
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
56322017
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
2082019
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
1012019
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
662020
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
162020
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
142020
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
142018
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
132020
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
132020
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 3, 2020
122020
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
92021
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
92020
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
32021
Accelerating Random Kaczmarz Algorithm Based on Clustering Information
Y Li, K Mo, H Ye
AAAI 2016, 2015
32015
Learning to Regrasp by Learning to Place
S Cheng, K Mo, L Shao
Conference on Robot Learning (CoRL) 2021, 2021
2021
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
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, ...
arXiv preprint arXiv:2106.14440, 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
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