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Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
Wen Shen · Qihan Ren · Dongrui Liu · Quanshi Zhang

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ Virtual

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. The code will be released when this paper is accepted.

Author Information

Wen Shen (Tongji University)
Qihan Ren (Shanghai Jiao Tong University)
Dongrui Liu (Shanghai Jiao Tong University,)
Quanshi Zhang (University of Tokyo)

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