Robust analysis of 3D point cloud data is essential for high-precision applications such as autonomous driving and industrial automation, where models need to consistently perform under complex and unpredictable real-world conditions. Current strategies, including data augmentation techniques and robust network designs, often struggle to effectively capture dynamic disturbances or accommodate the spatial variations of point clouds, thus lacking the required flexibility across diverse environments. To overcome these limitations, we propose a novel methodology to improve the robustness of 3D point cloud processing systems. Our approach simulates generalized corrupted input samples during training, using Radial Basis Functions (RBF) to model smooth deformations based on the control points. These deformations are applied selectively to different regions of the point cloud, adapting to spatial heterogeneity based on local density and geometric complexity. While generating these samples, we employ a combined adversarial loss that simultaneously induces model errors and maximizes the difference in internal feature distributions between the original and perturbed data. Additionally, we introduce a sub-network for distortion-guided feature augmentation to enhance important features while suppressing unreliable ones. This sub-network estimates distortion levels by compressing features and identifying discrepancies, then adjusts feature extraction process accordingly. Experimental results demonstrate that our method outperforms existing approaches on both Computer-Aided Design (CAD) models and real-world LiDAR datasets, enhancing model resilience and accuracy in handling diverse 3D scenarios.
He et al. (Wed,) studied this question.