Cross-Modal Information-Guided Network using Contrastive Learning for
  Point Cloud Registration
Yifan Xie Jihua Zhu Shiqi Li Pengcheng Shi
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.