A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation

TL;DR

This paper introduces SCT, a novel method that enhances point cloud recognition models with rotation and translation invariance, significantly improving robustness and accuracy in real-world scenarios involving arbitrary orientations.

Contribution

The paper proposes Self Contour-based Transformation (SCT), a flexible approach that provides rotation and translation invariance for point cloud recognition models, with theoretical proof and extensive experimental validation.

Findings

SCT outperforms state-of-the-art methods under arbitrary rotations.

SCT improves robustness and efficiency on synthetic and real-world benchmarks.

The method is applicable to various point cloud models, enhancing industrial applications.

Abstract

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.