Learning SO(3)-Invariant Semantic Correspondence via Local Shape
  Transform
Chunghyun Park Seungwook Sim Jaesik Park Minsu Cho
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.