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Learning to predict reliable characteristic orientations of 3D point cloudsis an important yet challenging problem, as different point clouds of the sameclass may have largely varying appearances. In this work, we introduce a novelmethod to decouple the shape geometry and semantics of the input point cloud toachieve both stability and consistency. The proposed method integratesshape-geometry-based SO(3)-equivariant learning and shape-semantics-basedSO(3)-invariant residual learning, where a final characteristic orientation isobtained by calibrating an SO(3)-equivariant orientation hypothesis using anSO(3)-invariant residual rotation. In experiments, the proposed method not onlydemonstrates superior stability and consistency but also exhibitsstate-of-the-art performances when applied to point cloud part segmentation,given randomly rotated inputs.

Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning