Deformable 3-D Point Cloud Perturbations Using Cage-Based Deformation for Semantic Consistency

Summary

Researchers propose a cage-based adversarial deformation framework that generates semantically consistent perturbations to 3D point cloud classifiers, aiming to resolve the trade-off between imperceptibility and attack strength found in point-wise distance methods. Experiments on ModelNet40, ShapeNet-Part and ScanObjectNN report consistently high attack success rates, improved point uniformity and fewer local geometric distortions, with the perturbations remaining effective against various defense methods.

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