A non‐rigid point cloud registration method based on scene flow estimation

Abstract Point cloud registration is an important aspect of computer vision, encompassing various applications. However, many existing algorithms neglect the registration of non‐rigid point cloud. Recent studies have focused on scene flow, which can achieve non‐rigid point cloud registration by estimating the scene flow of two point clouds. The presence of occlusion in a scene is primarily attributed to variations in viewpoint and access time, thereby posing a significant challenge in accurately predicting scene flow. In the endeavour to mitigate this issue, the focus is on the propagation of scene flow originating from non‐occluded points towards occlusion points while concurrently estimating the occlusion map. The proposed network, a non‐rigid point cloud registration method based on scene flow estimation, achieves exceptional performance for the EPE3D metric on the FlyingThings3D and KITTI scene flow datasets, and it demonstrates strong generalization on the railroad dataset.