Recurrent Non-Rigid Point Cloud Registration

Abstract

Non-rigid point cloud registration remains a significant challenge in 3D computer vision due to the complexity of structural deforms, lack of overlaps, and sensitivity to initialization. This paper introduces a framework inspired by the recent success in recurrent architecture, adapted to accommodate the unique characteristics of point clouds. More specifically, we design a recurrent update network block for progressively refining local registration results under a local rigidity assumption, starting from an initial global SE(3) alignment. Through comparison, our method consistently outperforms competing methods in standard metrics, achieving a 33% reduction in EPE on the 4DLoMatch benchmark compared to the second-best method. To the best of our knowledge, the proposed method is the first to successfully demonstrate that the recurrent update strategy can effectively address the non-rigid registration task with large displacement, significant deform, and low overlap. The source code and the model will be released at http://dummy.url/.