3DeformRS: Certifying Spatial Deformations on Point Clouds
This addresses security-critical applications like autonomous driving by offering a practical and reliable method for certifying robustness against deformations, though it builds incrementally on existing randomized smoothing techniques.
The authors tackled the problem of certifying the robustness of point cloud deep neural networks against real-world deformations, proposing 3DeformRS, which provides certificates 3x larger and 20x faster than previous methods for a specific case.
3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these models against real-world deformations must be addressed practically and reliably. In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations. We developed 3DeformRS by building upon recent work that generalized Randomized Smoothing (RS) from pixel-intensity perturbations to vector-field deformations. In particular, we specialized RS to certify DNNs against parameterized deformations (e.g. rotation, twisting), while enjoying practical computational costs. We leverage the virtues of 3DeformRS to conduct a comprehensive empirical study on the certified robustness of four representative point cloud DNNs on two datasets and against seven different deformations. Compared to previous approaches for certifying point cloud DNNs, 3DeformRS is fast, scales well with point cloud size, and provides comparable-to-better certificates. For instance, when certifying a plain PointNet against a 3° z-rotation on 1024-point clouds, 3DeformRS grants a certificate 3x larger and 20x faster than previous work.