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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 local surrogate model-based method to show which components contributeto the classification. Moreover, we propose quantitative fidelity validationsfor generated explanations that enhance the persuasive power of explainabilityand compare the plausibility of different existing point cloud-applicableexplainability methods. Our new explainability approach provides a fairlyaccurate, more semantically coherent and widely applicable explanation forpoint cloud classification tasks. Our code is available athttps://github.com/Explain3D/LIME-3D

Surrogate Model-Based Explainability Methods for Point Cloud NNs