Unsupervised Non-Rigid Point Cloud Shape Correspondence via Equivariant Local Reference Frames and Refinement

The core message of this work is to address the challenges of exponential complexity and out-of-distribution geometric contexts in unsupervised non-rigid point cloud shape correspondence by learning pair-wise independent SE(3)-equivariant Local Reference Frames (LRFs) and refining them to adapt to specific contexts.