Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation

arXiv:2308.04725v2 Announce Type: replace
Abstract: Invariance against rotations of 3D objects is an important property in analyzing 3D point set data. Conventional 3D point set DNNs having rotation invariance typically obtain accurate 3D shape features via supervised learning by using labeled 3D point sets as training samples. However, due to the rapid increase in 3D point set data and the high cost of labeling, a framework to learn rotation-invariant 3D shape features from numerous unlabeled 3D point sets is required. …

arxiv cs.cv cs.ir distillation features rotation self-supervised learning set supervised learning transformer type