Equivariant Local Reference Frames for Unsupervised Non-rigid Point Cloud Shape Correspondence

arXiv:2404.00959v1 Announce Type: new
Abstract: Unsupervised non-rigid point cloud shape correspondence underpins a multitude of 3D vision tasks, yet itself is non-trivial given the exponential complexity stemming from inter-point degree-of-freedom, i.e., pose transformations. Based on the assumption of local rigidity, one solution for reducing complexity is to decompose the overall shape into independent local regions using Local Reference Frames (LRFs) that are invariant to SE(3) transformations. However, the focus solely on local structure neglects global geometric contexts, resulting in less …

abstract arxiv cloud complexity cs.cv freedom reference solution stemming tasks type unsupervised vision