基于空间可变形Transformer的三维点云配准方法OA北大核心CSTPCD

Aiming at the problem of poor robustness and low registration accuracy of point cloud registration algorithms in low overlap scenarios,this study proposes a Three-Dimensional(3D)point cloud registration method based on a Spatial Deformable Transformer(SDT).A multi-level resolution feature extraction and fusion method is designed to explicitly compute the local spatial relationships of point clouds.The SDT module is used to enhance the expressive power of the spatial features of the point cloud,and local and global features are aggregated to obtain the feature matrix.This method computes the similarity matrix of the two feature matrices and adds an edge slack block to it,effectively reducing the impact of infeasible matching on the robustness of registration.Additionally,the similarity matrix is normalized and calculated to obtain the soft correspondence confidence matrix,and the more accurate correspondence of the point cloud in the low overlap scenario is determined according to whether the spatial features of the corresponding points are consistent.The loss defined directly on the correspondence is used to train the network to convert the soft correspondence into a one-to-one hard matching relation,and finally,the registration is performed by the RANdom SAmple Consensus(RANSAC)rigid transformation solver.Experimental results demonstrate that the Feature Matching Recall(FMR)and Registration Recall(RR)of the proposed method are at least 3.7 and 3.9 percentage points higher than those of Pairwise pointcloud REgistration with Deep Attention To the Overlap Region(PREDATOR),respectively,including other methods in the 3DLoMatch scenario with less than 30%overlap,with strong robustness.