Learning to predict reliable characteristic orientations of 3D point clouds
is an important yet challenging problem, as different point clouds of the same
class may have largely varying appearances. In this work, we introduce a novel
method to decouple the shape geometry and semantics of the input point cloud to
achieve both stability and consistency. The proposed method integrates
shape-geometry-based SO(3)-equivariant learning and shape-semantics-based
SO(3)-invariant residual learning, where a final characteristic orientation is
obtained by calibrating an SO(3)-equivariant orientation hypothesis using an
SO(3)-invariant residual rotation. In experiments, the proposed method not only
demonstrates superior stability and consistency but also exhibits
state-of-the-art performances when applied to point cloud part segmentation,
given randomly rotated inputs.

本文提出了一种新颖的方法，通过将输入的 3D 点云的形状几何与语义学分离，来实现特征取向的稳定性和一致性，该方法将形状几何基于 SO（3）等变学习和形状语义基于 SO（3）不变残差学习相结合，通过校准 SO（3）等变定向假设来获得最终的特征定向。在实验中，该方法不仅展现出卓越的稳定性和一致性，而且在给定随机旋转输入的点云部分分割时也表现出最新的性能。