  To eliminate the problems of large dimensional differences, big semantic gap,
and mutual interference caused by hybrid features, in this paper, we propose a
novel Multi-Features Guidance Network for partial-to-partial point cloud
registration(MFG). The proposed network mainly includes four parts: keypoints'
feature extraction, correspondences searching, correspondences credibility
computation, and SVD, among which correspondences searching and correspondence
credibility computation are the cores of the network. Unlike the previous work,
we utilize the shape features and the spatial coordinates to guide
correspondences search independently and fusing the matching results to obtain
the final matching matrix. In the correspondences credibility computation
module, based on the conflicted relationship between the features matching
matrix and the coordinates matching matrix, we score the reliability for each
correspondence, which can reduce the impact of mismatched or non-matched
points. Experimental results show that our network outperforms the current
state-of-the-art while maintaining computational efficiency.

Computer Vision and Pattern Recognition