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.

翻译：在这份文件中,我们评估了3D点云处理在深神经网络中编码的知识表现的质量,我们提出了一种方法,将整个模型的脆弱性与对轮换、翻译、规模和地方3D结构的敏感程度分离开来。此外,我们还提出了衡量标准,以评估编码3D结构的空间平稳性以及DNN的复杂代表性。根据这种分析,实验暴露了典型DN的表述问题,并解释了对抗性培训的效用。