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Interpreting Representation Quality of DNNs for 3D Point Cloud Processing

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arxiv 2111.03549 v1 pith:LJTLIZYT submitted 2021-11-05 cs.CV cs.AIcs.LG

Wen Shen , Qihan Ren , Dongrui Liu , Quanshi Zhang This is my paper
classification cs.CVcs.AIcs.LG
keywords dnnsrepresentationcloudevaluatepointprocessingproposequality
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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.

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Cited by 1 Pith paper

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  1. XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A new XAI method for point cloud classification uses semantic segmentation to define meaningful perturbation regions and a point-shifting mechanism to compute saliency maps.

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