SeLCA: Self-Supervised Learning of Canonical Axis

Abstract

Robustness to rotation is critical for point cloud understanding tasks as point cloud features can be affected dramatically with respect to prevalent rotation changes. In this work, we introduce a novel self-supervised learning framework, dubbed SeLCA, that predicts a canonical axis of point clouds in a probabilistic manner. In essence, we propose to learn rotational-equivariance by predicting the canonical axis of point clouds, and achieve rotational-invariance by aligning the point clouds using their predicted canonical axis. When integrated into a rotation-sensitive pipeline, SeLCA achieves competitive performances on the ModelNet40 classification task under unseen rotations. Our proposed method also shows high robustness to various real-world point cloud corruptions presented by the ModelNet40-C dataset, compared to the state-of-the-art rotation-invariant method.