We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to discriminate outliers from inliers. It measures the compatibility of two correspondences by the discrepancy between the respective distances in two point clouds. However, spatial consistency no longer holds in non-rigid cases and outlier rejection for non-rigid registration has not been well studied. In this work, we propose Graph-based Spatial Consistency Network (GraphSCNet) to filter outliers for non-rigid registration. Our method is based on the fact that non-rigid deformations are usually locally rigid, or local shape preserving. We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node. An attention-based non-rigid correspondence embedding module is then devised to learn a robust representation of non-rigid correspondences from local spatial consistency. Despite its simplicity, GraphSCNet effectively improves the quality of the putative correspondences and attains state-of-the-art performance on three challenging benchmarks. Our code and models are available at https://github.com/qinzheng93/GraphSCNet.

翻译：我们研究非刚性点云配准中的离群对应点剔除问题。在刚性配准中，空间一致性常被用作区分离群点与内点的准则，通过测量两个点云中对应点对之间的距离差异来评估其兼容性。然而，空间一致性在非刚性情形下不再成立，且非刚性配准的离群点剔除尚未得到充分研究。本文提出基于图的空间一致性网络(GraphSCNet)以筛选非刚性配准中的离群对应点。该方法基于以下事实：非刚性形变通常具有局部刚性或局部形状保持特性。我们首先在点云形变图上设计局部空间一致性度量，仅评估图节点邻域内对应点对之间的空间兼容性。进而构建基于注意力机制的非刚性对应点嵌入模块，从局部空间一致性中学习鲁棒的非刚性对应点表示。尽管结构简洁，GraphSCNet能有效提升候选对应点质量，并在三个具有挑战性的基准测试中取得最优性能。相关代码与模型已开源至https://github.com/qinzheng93/GraphSCNet。