Object retrieval and classification in point cloud data is challenged by noise, irregular sampling density and occlusion. To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy. We further show how the proposed descriptor can be used in a 4D convolutional neural network for the task of object classification. We propose a novel 4D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on 3 benchmark datasets, which confirms the superiority of the proposed approach.
@article{arxiv.1804.02077,
title = {Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor},
author = {Dmytro Bobkov and Sili Chen and Ruiqing Jian and Muhammad Iqbal and Eckehard Steinbach},
journal= {arXiv preprint arXiv:1804.02077},
year = {2018}
}