Abstract:

Compared with the pair-wise registration of point clouds, multi-view point cloud registration is much less studied. In this dissertation, a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed. In this method, firstly, the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature. Secondly, neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods. By combining distance consistency and high dimensional feature consistency, our network introduces the confidence estimation module of registration, so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem, achieving the pair-wise registration of partially overlapping point clouds. Thirdly, the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds. Finally, the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately. This method achieves rough point cloud registration quickly in the early stage, improves the accuracy of multi-view point cloud registration in the later stage, and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

Key words: soft trimmed deep network, point cloud, registration, hierarchical clustering