  Current models for point cloud recognition demonstrate promising performance
on synthetic datasets. However, real-world point cloud data inevitably contains
noise, impacting model robustness. While recent efforts focus on enhancing
robustness through various strategies, there still remains a gap in
comprehensive analyzes from the standpoint of network architecture design.
Unlike traditional methods that rely on generic techniques, our approach
optimizes model robustness to noise corruption through network architecture
design. Inspired by the token-mixing technique applied in 2D images, we propose
Set-Mixer, a noise-robust aggregation module which facilitates communication
among all points to extract geometric shape information and mitigating the
influence of individual noise points. A sorting strategy is designed to enable
our module to be invariant to point permutation, which also tackles the
unordered structure of point cloud and introduces consistent relative spatial
information. Experiments conducted on ModelNet40-C indicate that Set-Mixer
significantly enhances the model performance on noisy point clouds,
underscoring its potential to advance real-world applicability in 3D
recognition and perception tasks.
