Classify 3D Point Clouds
4 papers with code • 0 benchmarks • 2 datasets
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Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.
This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds.
Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network.
Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of considerations that have been united under the banner of geometric deep learning.