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.

翻译：学习预测三维点云的可靠特征方向是一个重要但充满挑战的问题，因为同一类别的不同点云可能在外观上存在显著差异。本文提出了一种新颖方法，通过解耦输入点云的形状几何与语义信息，实现稳定性和一致性。该方法融合了基于形状几何的SO(3)-等变学习与基于形状语义的SO(3)-不变残差学习，通过使用SO(3)-不变残差旋转校准SO(3)-等变方向假设，最终获得特征方向。实验表明，该方法不仅展现出优越的稳定性和一致性，而且在随机旋转输入的点云部件分割任务中取得了最先进的性能表现。