  In this paper, we evaluate the quality of knowledge representations encoded
in deep neural networks (DNNs) for 3D point cloud processing. We propose a
method to disentangle the overall model vulnerability into the sensitivity to
the rotation, the translation, the scale, and local 3D structures. Besides, we
also propose metrics to evaluate the spatial smoothness of encoding 3D
structures, and the representation complexity of the DNN. Based on such
analysis, experiments expose representation problems with classic DNNs, and
explain the utility of the adversarial training.
