Establishing accurate 3D correspondences between shapes stands as a pivotal
challenge with profound implications for computer vision and robotics. However,
existing self-supervised methods for this problem assume perfect input shape
alignment, restricting their real-world applicability. In this work, we
introduce a novel self-supervised Rotation-Invariant 3D correspondence learner
with Local Shape Transform, dubbed RIST, that learns to establish dense
correspondences between shapes even under challenging intra-class variations
and arbitrary orientations. Specifically, RIST learns to dynamically formulate
an SO(3)-invariant local shape transform for each point, which maps the
SO(3)-equivariant global shape descriptor of the input shape to a local shape
descriptor. These local shape descriptors are provided as inputs to our decoder
to facilitate point cloud self- and cross-reconstruction. Our proposed
self-supervised training pipeline encourages semantically corresponding points
from different shapes to be mapped to similar local shape descriptors, enabling
RIST to establish dense point-wise correspondences. RIST demonstrates
state-of-the-art performances on 3D part label transfer and semantic keypoint
transfer given arbitrarily rotated point cloud pairs, outperforming existing
methods by significant margins.

提出了一种自我监督的旋转不变三维对应学习器，在具有挑战性的类内差异和任意方向下学习建立形状间的密集对应关系。