Summary of Generalizable Articulated Object Perception with Superpoints, by Qiaojun Yu et al.
Generalizable Articulated Object Perception with Superpoints
by Qiaojun Yu, Ce Hao, Xibin Yuan, Li Zhang, Liu Liu, Yukang Huo, Rohit Agarwal, Cewu Lu
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel perception method is introduced to improve part segmentation in 3D point clouds of articulated objects, enhancing robotic arm manipulation. The superpoint-based approach groups points by geometric and semantic similarities, providing clearer part boundaries. By leveraging the SAM foundation model and a query-based transformer decoder, the method achieves precise part segmentation. Experimental results on the GAPartNet dataset show significant improvements over existing approaches in cross-category part segmentation, with AP50 scores of 77.9% for seen categories and 39.3% for unseen categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to identify parts of objects that are connected by joints. This is important because it helps robots move these objects more precisely. The method uses special points called “superpoints” that are grouped together based on how similar they are in shape and meaning. It also uses another tool to find the center of each group and choose which superpoints to look at first. Tests showed that this new method is better than others at identifying parts, especially for objects it hasn’t seen before. |
Keywords
» Artificial intelligence » Decoder » Sam » Transformer
