|
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object UnderstandingKaichun Mo, Shilin Zhu, Angel X.Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas and Hao SuarXiv:1812.02713 [cs.CV] We propose a 3D object database with fine-grained and hierarchical part annotation, to assist segmentation and affordance research. We benchmark three part-level object understanding tasks: fine-grained semantic segmentation, hierarchical semantic segmentation and instance segmentation. We also propose a novel method for instance segmentation. [Paper] [Project] [Bibtex] |
|
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual NavigationKaichun Mo, Haoxiang Li, Zhe Lin and Joon-Young LeearXiv:1802.08824 [cs.RO] We propose an indoor navigation dataset for visual navigation and deep reinforcement learning research. We show that the current mapless DRL-based method suffers from the target generalization and scene generalization issues. We propose methods to improve target generalization. [Paper] [Project] [Bibtex] |
|
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCharles R. Qi*, Hao Su*, Kaichun Mo and Leonidas J. GuibasCVPR 2017, Oral Presentation We propose novel neural networks to directly consume an unordered point cloud as input, without converting to other 3D representations such as voxel grids first. Rich theoretical and empirical analyses are provided. [Paper] [Project] [Bibtex] |
|
Accelerating Random Kaczmarz Algorithm Based on Clustering InformationYujun Li, Kaichun Mo and Haishan YeAAAI 2016 Using the property of randomly sampled data in high-dimensional space, we propose an accelerated algorithm based on clustering information to improve block Kaczmarz and Kaczmarz via Johnson-Lindenstrauss lemma. Additionally, we theoretically demonstrate convergence improvement on block Kaczmarz algorithm. [Paper] [Bibtex] |
| (*: indicates equal contribution.) |
Kaichun Mo 莫凯淳
Email: kaichun [at] cs [dot] stanford [dot] edu
Office: S297 James H. Clark Center
Office: S297 James H. Clark Center
