Surrogate Model-Based Explainability Methods for Point Cloud NNs

Hanxiao Tan, Helena Kotthaus; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 2239-2248

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 approaches based on local surrogate model-based methods to show which components make the main contribution 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 intuitive and widely applicable explanation for point cloud classification tasks. Our code is available at https://github.com/Explain3D/LIME-3D