  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.
