We propose a fast and simple explainable AI (XAI) method for point cloud
data. It computes pointwise importance with respect to a trained network
downstream task. This allows better understanding of the network properties,
which is imperative for safety-critical applications. In addition to debugging
and visualization, our low computational complexity facilitates online feedback
to the network at inference. This can be used to reduce uncertainty and to
increase robustness. In this work, we introduce \emph{Feature Based
Interpretability} (FBI), where we compute the features' norm, per point, before
the bottleneck. We analyze the use of gradients and post- and pre-bottleneck
strategies, showing pre-bottleneck is preferred, in terms of smoothness and
ranking. We obtain at least three orders of magnitude speedup, compared to
current XAI methods, thus, scalable for big point clouds or large-scale
architectures. Our approach achieves SOTA results, in terms of classification
explainability. We demonstrate how the proposed measure is helpful in analyzing
and characterizing various aspects of 3D learning, such as rotation invariance,
robustness to out-of-distribution (OOD) outliers or domain shift and dataset
bias.

我们提出了一种快速简单的解释型 AI (XAI) 方法，用于点云数据。通过计算针对训练网络的点级重要性，可以更好地理解网络属性，这对于安全关键应用至关重要。除了调试和可视化外，我们的低计算复杂性还有助于在线反馈网络推断。这可以用于减少不确定性并增强鲁棒性。我们的方法在分类解释性方面取得了 SOTA 结果，同时还展示了所提措施如何有助于分析和表征 3D 学习的各个方面，例如旋转不变性、对分布外离群值或领域转移和数据集偏差的鲁棒性。