  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.
