OAAFormer: Robust and Efficient Point Cloud Registration Through
  Overlapping-Aware Attention in Transformer
Junjie Gao Qiujie Dong Ruian Wang Shuangmin Chen Shiqing Xin Changhe Tu Wenping Wang
In the domain of point cloud registration, the coarse-to-fine feature
matching paradigm has received substantial attention owing to its impressive
performance. This paradigm involves a two-step process: first, the extraction
of multi-level features, and subsequently, the propagation of correspondences
from coarse to fine levels. Nonetheless, this paradigm exhibits two notable
limitations.Firstly, the utilization of the Dual Softmax operation has the
potential to promote one-to-one correspondences between superpoints,
inadvertently excluding valuable correspondences. This propensity arises from
the fact that a source superpoint typically maintains associations with
multiple target superpoints. Secondly, it is imperative to closely examine the
overlapping areas between point clouds, as only correspondences within these
regions decisively determine the actual transformation. Based on these
considerations, we propose {\em OAAFormer} to enhance correspondence quality.
On one hand, we introduce a soft matching mechanism, facilitating the
propagation of potentially valuable correspondences from coarse to fine levels.
Additionally, we integrate an overlapping region detection module to minimize
mismatches to the greatest extent possible. Furthermore, we introduce a
region-wise attention module with linear complexity during the fine-level
matching phase, designed to enhance the discriminative capabilities of the
extracted features. Tests on the challenging 3DLoMatch benchmark demonstrate
that our approach leads to a substantial increase of about 7\% in the inlier
ratio, as well as an enhancement of 2-4\% in registration recall. =