  The majority of point cloud registration methods currently rely on extracting
features from points. However, these methods are limited by their dependence on
information obtained from a single modality of points, which can result in
deficiencies such as inadequate perception of global features and a lack of
texture information. Actually, humans can employ visual information learned
from 2D images to comprehend the 3D world. Based on this fact, we present a
novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global
shape perception through cross-modal information to achieve precise and robust
point cloud registration. Specifically, we first incorporate the projected
images from the point clouds and fuse the cross-modal features using the
attention mechanism. Furthermore, we employ two contrastive learning
strategies, namely overlapping contrastive learning and cross-modal contrastive
learning. The former focuses on features in overlapping regions, while the
latter emphasizes the correspondences between 2D and 3D features. Finally, we
propose a mask prediction module to identify keypoints in the point clouds.
Extensive experiments on several benchmark datasets demonstrate that our
network achieves superior registration performance.
