Summary of Geometric Point Attention Transformer For 3d Shape Reassembly, by Jiahan Li et al.
Geometric Point Attention Transformer for 3D Shape Reassembly
by Jiahan Li, Chaoran Cheng, Jianzhu Ma, Ge Liu
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 deep learning framework, dubbed Geometric Point Attention Transformer (GPAT), is proposed to tackle the challenging task of shape assembly. GPAT addresses limitations in existing methods by explicitly modeling geometric relationships between parts and their poses. The network integrates global shape information, local pairwise geometric features, and pose representations (rotation and translation vectors) within its geometric point attention module. An iterative refinement mechanism, called geometric recycling, is introduced to enable dynamic reasoning. Experimental evaluations on both semantic and geometric assembly tasks demonstrate GPAT’s superiority over previous methods in absolute pose estimation, achieving accurate pose predictions and high alignment accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shape assembly is a complex problem where separate parts are reassembled into a complete object. Currently, networks predict the poses of individual parts, but struggle to understand how these parts interact with each other. A new approach called GPAT solves this issue by considering geometric relationships between parts and their positions. This network combines information about the shape, local interactions between parts, and their movements (rotation and translation). It also refines its predictions through an iterative process. The results show that GPAT is better than previous methods at accurately predicting part positions and aligning them correctly. |
Keywords
» Artificial intelligence » Alignment » Attention » Deep learning » Pose estimation » Transformer » Translation
