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PointGuard: Provably Robust 3D Point Cloud Classification

CVPR 2021 · Point clouds › Point cloud classification · approach: Adversarial point cloud attacks · 62 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PointGuard with subsampling instead of randomized smoothingPointNet · ModelNet40, point modification, 30 pointscertified accuracy: 0% -> 69.7%Helped
Train the classifier on subsampled point clouds rather than original point clouds (ablation)PointGuard · ModelNet40certified accuracyHelped
Reduce subsampling size k (ablation)PointGuard · ModelNet40certified accuracyMixed
Increase confidence-bound parameter alpha (ablation)PointGuard · ModelNet40certified accuracyHelped
Increase Monte Carlo sample count N (ablation)PointGuard · ModelNet40certified accuracyHelped
Increase alpha or N after they are already large enough (ablation)PointGuard · ModelNet40certified accuracyNo clear effect
Use PointGuard rather than randomized smoothingPointNet · ModelNet40, point modification attackscertified accuracyHelped
Apply PointGuard to point deletion rather than relying on the empirical deletion attack resultPointNet · ModelNet40, point deletion attacksaccuracy: 76.1% vs. 73.4%; 67.7% vs. 64.3%; 57.7% vs. 53.5%Mixed

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