29 citations · 67 across the 9 of their papers we have counts for
12 papers
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
Yongyi Su, Xun Xu, Kui Jia
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. Existing approaches mainly focus on exploiting manifold an…
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
Shengheng Deng, Xun Xu, Chaozheng Wu +2
The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing,…
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Xian Shi, Xun Xu, Ke Chen +3
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. Howe…
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
Chaozheng Wu, Lin Sun, Xun Xu +1
This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair…
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
Xun Xu, Gim Hee Lee
Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network des…
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models
Xun Xu, Loong-Fah Cheong, Zhuwen Li
Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography…