In recent years, the feature-based point cloud registration
methods have attracted more attention. However, most existing
methods focus on extracting features with strong antiinterference
ability from a single point cloud while neglecting
the differences within point cloud pairs. In this paper, unlike
these methods treating each point cloud independently, we
instead consider the information between point cloud pairs
when extracting features. Specifically, we propose a crossattention-
based network for modeling the correlation between
a pair of point clouds, where a 3D cross-attention mechanism
is proposed and combined with 3D convolution elegantly for
feature extraction. The extracted features achieve better robustness
under various conditions, such as rotation and translation
changes. Then accurate point cloud registration is
achieved by matching these features. Experimental results
on 3DMatch dataset show that the proposed method achieves
state-of-the-art performance on feature matching and point
cloud registration tasks compared with the previous featurebased
methods.