  In the field of autonomous driving and robotics, point clouds are showing
their excellent real-time performance as raw data from most of the mainstream
3D sensors. Therefore, point cloud neural networks have become a popular
research direction in recent years. So far, however, there has been little
discussion about the explainability of deep neural networks for point clouds.
In this paper, we propose a point cloud-applicable explainability approach
based on a local surrogate model-based method to show which components
contribute to the classification. Moreover, we propose quantitative fidelity
validations for generated explanations that enhance the persuasive power of
explainability and compare the plausibility of different existing point
cloud-applicable explainability methods. Our new explainability approach
provides a fairly accurate, more semantically coherent and widely applicable
explanation for point cloud classification tasks. Our code is available at
https://github.com/Explain3D/LIME-3D
