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In the field of autonomous driving and robotics, point clouds are showingtheir excellent real-time performance as raw data from most of the mainstream3D sensors. Therefore, point cloud neural networks have become a popularresearch direction in recent years. So far, however, there has been littlediscussion about the explainability of deep neural networks for point clouds.In this paper, we propose a point cloud-applicable explainability approachbased on a local surrogate model-based method to show which componentscontribute to the classification. Moreover, we propose quantitative fidelityvalidations for generated explanations that enhance the persuasive power ofexplainability and compare the plausibility of different existing pointcloud-applicable explainability methods. Our new explainability approachprovides a fairly accurate, more semantically coherent and widely applicableexplanation for point cloud classification tasks. Our code is available athttps://github.com/Explain3D/LIME-3D

Visualizing Global Explanations of Point Cloud DNNs