Multi-Features Guidance Network for partial-to-partial point cloud registration
This work provides an incremental improvement in point cloud registration for computer vision and robotics applications, specifically for handling partial-to-partial data.
This paper addresses partial-to-partial point cloud registration by proposing a Multi-Features Guidance Network (MFG) that independently guides correspondence search using shape features and spatial coordinates, then fuses the results. It also computes correspondence credibility to reduce the impact of mismatches. The network achieves state-of-the-art performance while maintaining computational efficiency.
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.