Kaichun Mo 莫凯淳

kaichun@cs.stanford.edu
personal page

Research interests

My research interests include Computer Vision and Graphics, Robotics, Machine Learning and Artificial Intelligence. In particular, I am interested in 3D vision and graphics, deep learning, geometry processing, reinforcement learning and robotics.

Recent publications

Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas and Hao Dong, Generative 3D Part Assembly via Dynamic Graph Learning, NeurIPS 2020   
Kaichun Mo, He Wang, Xinchen Yan, and Leonidas J. Guibas, PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions, European Conference on Computer Vision (ECCV) 2020   
Yichen Li*, Kaichun Mo*, Lin Shao, Minhyuk Sung, and Leonidas J. Guibas, Learning 3D Part Assembly from a Single Image, European Conference on Computer Vision (ECCV) 2020   
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X.Chang, Leonidas J. Guibas and Hao Su, SAPIEN: A SimulAted Part-based Interactive ENvironment. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Oral)   
Kaichun Mo*, Paul Guerrero*, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas J. Guibas, StructEdit: Learning Structural Shape Variations, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.   
Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Liwei Wang and Hao Su, Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories, ICLR 2020   
Kaichun Mo*, Paul Guerrero*, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas J. Guibas, StructureNet: Hierarchical Graph Networks for 3D Shape Generation, Siggraph Asia 2019   
Kaichun Mo, Shilin Zhu, Angel Chang, Li Yi, Subarna Tripathi, Leonidas Guibas, and Hao Su, PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding, CVPR 2019   
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, Honolulu USA.