Robust Context Modeling for Unsupervised Non-Rigid Point Cloud Correspondence

We address the “long-range ambiguity” problem for unsupervised non-rigid point cloud correspondence, where corresponding points own inconsistent features while different local regions are spatially or geometrically similar. Previous methods struggle with this problem, since local reference frames (LRF) or coordinate-based methods struggle to exclude locally similar or spatially near mismatches, and widely used independent geometric relations might be inconsistent under non-rigid deformation, introducing extra ambiguity. To this end, we propose a novel robust context modeling module (RCM) to alleviatelong-range ambiguityin two aspects: 1) RCM tackles the ambiguity problem by introducing inter-relation attention (IRA), which mines robust cues from the interplay between relative geometric relations. 2) RCM enhances features with accessiblelong-rangeinformation from IRAs, following a local-to-global manner. Our method shows significant improvements in multiple benchmarks, with accurate correspondence over rotation and large deformation perturbation. Specifically, our method achieves a new state-of-the-art performance with correspondence accuracy of 33.9% and mean error of 4.2 on the SURREAL benchmark.