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Point Tree Transformer for Point Cloud Registration by Wang, Meiling; Chen, Guangyan; Yang, Yi; Yuan, Li; Yue, Yufeng is a scholarly article available to read on EtoBox.

What is Point Tree Transformer for Point Cloud Registration about?

Point cloud registration is a fundamental task in the fields of computer vision and robotics. Recent developments in transformer-based methods have demonstrated enhanced performance in this domain. However, the standard attention mechanism utilized in these methods often integrates many low-relevance points, thereby struggling to prioritize its attention weights on sparse yet meaningful points. This inefficiency leads to limited local structure modeling capabilities and quadratic computational complexity. To overcome these limitations, we propose the Point Tree Transformer (PTT), a novel transformer-based approach for point cloud registration that efficiently extracts comprehensive local and global features while maintaining linear computational complexity. The PTT constructs hierarchical feature trees from point clouds in a coarse-to-dense manner, and introduces a novel Point Tree Attention (PTA) mechanism, which follows the tree structure to facilitate the progressive convergence of attended regions towards salient points. Specifically, each tree layer selectively identifies a subset of key points with the highest attention scores. Subsequent layers focus attention on areas of si

Author

Wang, Meiling; Chen, Guangyan; Yang, Yi; Yuan, Li; Yue, Yufeng

Published

2024

Language

EN