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 significant relevance, derived from the child points of the selected point
set. The feature extraction process additionally incorporates coarse point
features that capture high-level semantic information, thus facilitating local
structure modeling and the progressive integration of multiscale information.
Consequently, PTA empowers the model to concentrate on crucial local structures
and derive detailed local information while maintaining linear computational
complexity. Extensive experiments conducted on the 3DMatch, ModelNet40, and
KITTI datasets demonstrate that our method achieves superior performance over
the state-of-the-art methods.

点云配准中的局部和全局特征提取是一个常见问题。本文提出了一种名为 “Point Tree Transformer (PTT)” 的新型基于 Transformer 的方法，通过构建点云的层次特征树和引入一种新的点树注意力机制（PTA）来提取细粒度的局部和全局特征，从而有效地解决低相关性点引起的性能下降和计算复杂度过高的问题。实验证明，该方法在 3DMatch、ModelNet40 和 KITTI 数据集上取得了优越的性能。