Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor

Object retrieval and classification in point cloud data are 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 four-dimensional (4-D) convolutional neural network for the task of object classification. We propose a novel 4-D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on three benchmark datasets, which confirms the superiority of the proposed approach.