MATR: Multicompound Adaptive Transformer for Point Cloud Registration

Point cloud registration plays a key role in the fields of computer vision, particularly in scenarios with low overlap, large scenes, different datasets, where difficulties, such as difficulty matching, scale changes and geometric deformations, local feature loss are commonly encountered. In this article, we propose a point cloud registration algorithm named multicompound adaptive transformer, which introduces adaptive position encoding, dynamically adjusting the local coordinates and feature information of scattered points within the point cloud through an adaptive threshold enhancement mechanism. Simultaneously, the multicompound transformer is introduced. In the spatial transformer stage, it accomplishes the local position-local feature interaction of individual point clouds through adaptive position encoding. Then, in the temporal transformer stage, it achieves local–local interaction and local–global information interaction between two point clouds through a dual-branch multiscale transformer. Through experiments on different datasets, we validate the algorithm's superior generalization performance in scenarios with low overlap, large scenes, and different datasets.