A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation

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 the 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 the discriminative feature extraction by capturing contours and transforming self-contour-based frames into intraclass 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.

Automated summary

Point cloud recognition is important in industrial robotics and autonomous driving. This study proposes a method called self-contour-based transformation (SCT) to address the issue of rotation robustness in point cloud recognition. Experimental results demonstrate that SCT outperforms existing methods in terms of effectiveness, efficiency, robustness, and generality in both synthetic and real-world scenarios.