Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation. (arXiv:2308.04725v1 [cs.CV])

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. This paper proposes a novel …

3d objects arxiv data distillation features objects property rotation self-supervised learning set supervised learning training transformer