Follow
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
74782017
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
2832019
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
1412019
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
1122020
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
222021
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
222020
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
202020
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
202020
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
182020
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
172020
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
172018
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
162020
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
82021
Learning to Regrasp by Learning to Place
S Cheng, K Mo, L Shao
Conference on Robot Learning (CoRL) 2021, 2021
42021
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
42021
Accelerating Random Kaczmarz Algorithm Based on Clustering Information
Y Li, K Mo, H Ye
AAAI 2016, 2015
42015
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
22020
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
22020
Fixing malfunctional objects with learned physical simulation and functional prediction
Y Hong, K Mo, L Yi, LJ Guibas, A Torralba, JB Tenenbaum, C Gan
arXiv preprint arXiv:2205.02834, 2022
12022
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
M Yu, L Shao, Z Chen, T Wu, Q Fan, K Mo, H Dong
arXiv preprint arXiv:2112.10143, 2021
12021
The system can't perform the operation now. Try again later.
Articles 1–20