  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 surface (compactness). We achieve these desiderata by
proposing a new self-supervised training strategy for keypoints estimation that
does not assume any a priori knowledge of the object class, and a model
architecture with coupled auxiliary losses that promotes the desired keypoints
properties. We compare the keypoints estimated by the proposed approach with
those of the state-of-the-art unsupervised approaches. The experiments show
that our approach outperforms by estimating keypoints with improved coverage
(+9.41%) while being semantically consistent (+4.66%) that best characterizes
the object's 3D shape for downstream tasks. Code and data are available at:
https://github.com/IITPAVIS/SC3K
