SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data. (arXiv:2308.05410v1 [cs.CV])

This paper proposes a new method to infer keypoints from arbitrary object
categories in practical scenarios where point cloud data (PCD) are noisy,
down-sampled and arbitrarily rotated. Our proposed model adheres to the
following principles: i) keypoints inference is fully unsupervised (no
annotation given), ii) keypoints position error should be low and resilient to
PCD perturbations (robustness), iii) keypoints should not change their indexes
for the intra-class objects (semantic coherence), iv) keypoints should be close
to or proximal to PCD …

annotation arxiv cloud cloud data data inference paper practical unsupervised