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Dec 24, 2019 · Motivated by the different mechanisms by which humans perceive 2D images and 3D shapes, in this paper we propose the new design of \emph{ ...
Sep 25, 2019 · This paper studies adversarial generation of point clouds by learning to deform those approximating object surfaces of certain categories. As 2D ...
Model tranining and data preparing. Use python main.py to train a new model. Here is an example settings for PointNet:.
Missing: Cooperative | Show results with:Cooperative
May 14, 2020 · Abstract—Machine learning models are shown to be vulnerable to adversarial examples. While most of the existing methods for.
Missing: Cooperative | Show results with:Cooperative
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[1] combined benign point clouds and adversarial point ... cooperative benefits. Suppose there are multiple ... et al. Geometry-aware generation of adversarial ...
Sep 17, 2021 · In this paper, we propose a unified formulation for mini- mal adversarial 3D point clouds generation that can gener- alise two attack strategies ...
This work proposes adversarial attacks based on solving different optimization problems, like minimizing the perceptibility of the authors' generated ...
We launch black-box attacks to validate our claim that degrades adversarially trained models' robust accuracy to merely ∼10%, which is no longer useful for 3D ...
Jun 23, 2024 · Recent work has proposed a general spoofing attack on LiDAR-based perception, based on the defect of ignored occlusion patterns in point clouds.
A point cloud is a simple and concise 3D representation, but point cloud generation is a long-term challenging task in 3D vision.