A new method for two-stage partial-to-partial 3D point cloud registration: multi-level interaction perception

3D point cloud registration related to rigid transformation is a fundamental yet crucial task in computer vision and graphics. For rigid registration, the local alignment of two-point clouds is equivalent to a global alignment. Since sufficient information exchange is an effective way to enhance mutual understanding, it is necessary to design a reasonable and sufficient feature interaction across two-point clouds to to obtain discriminative features and explore overlapping points. Recently, although a series of learning-based registration methods have been explored, most of the existing methods lack attention to multi-level feature interactions. In addition, there seems to be no paper that explicitly proposes a method for two-stage registration. However, intermediate constraints can be set in the two-stage registration to supervise the coarse registration and better refine the fine registration. To this end, this paper proposes a multi-level interaction perception method for two-stage partial-to-partial point cloud registration that can hierarchically capture discriminative structural features by the interaction of local details and global features from different dimensions, as well as improve the perception of locality in the early information exchange. Also, a spatial overlap-aware transformer is constructed to highlight the common regions while perceiving the global information of the point cloud. Thus, overlap constraints with high confidence between source and target point clouds can be obtained. The registration evaluation is performed on numerous partial 3D point clouds with Gaussian noise, and the results reveal that our method can achieve superior performance.