Abstract
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.
Motasem Alfarra
Machine Learning Researcher at Qualcomm AI Research, Amsterdam, Netherlands
I am a machine learning researcher at Qualcomm AI Research in Amsterdam, Netherlands. I obtained my Ph.D. in Electrical and Computer Engineering from KAUST in Saudi Arabia advised by Prof. Bernard Ghanem. I also obtained my M.Sc degree in Electrical Engineering from KAUST, and my undergraduate degree in Electrical Engineering from Kuwait University. I am interested in domain shifts, LLM safety, and how to combat them with test-time adaptation and continual learning. I helped co-organizing the first workshop on Test-Time Adaptation at CVPR2024!