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
41882017
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
1482019
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
622019
Pointnet: Deep learning on point sets for 3d classification and segmentation
K Mo, CR Qi, H Su, LJ Guibas
Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 652-660, 2017
512017
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
272020
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
152018
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
92020
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
82020
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
62020
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
62020
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
32020
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
32020
Accelerating Random Kaczmarz Algorithm Based on Clustering Information
Y Li, K Mo, H Ye
AAAI 2016, 2015
32015
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
12021
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
12020
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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