  Point cloud recognition is an essential task in industrial robotics and
autonomous driving. Recently, several point cloud processing models have
achieved state-of-the-art performances. However, these methods lack rotation
robustness, and their performances degrade severely under random rotations,
failing to extend to real-world scenarios with varying orientations. To this
end, we propose a method named Self Contour-based Transformation (SCT), which
can be flexibly integrated into various existing point cloud recognition models
against arbitrary rotations. SCT provides efficient rotation and translation
invariance by introducing Contour-Aware Transformation (CAT), which linearly
transforms Cartesian coordinates of points to translation and
rotation-invariant representations. We prove that CAT is a rotation and
translation-invariant transformation based on the theoretical analysis.
Furthermore, the Frame Alignment module is proposed to enhance discriminative
feature extraction by capturing contours and transforming self contour-based
frames into intra-class frames. Extensive experimental results show that SCT
outperforms the state-of-the-art approaches under arbitrary rotations in
effectiveness and efficiency on synthetic and real-world benchmarks.
Furthermore, the robustness and generality evaluations indicate that SCT is
robust and is applicable to various point cloud processing models, which
highlights the superiority of SCT in industrial applications.
