  Semantic Segmentation (SS) of LiDAR point clouds is essential for many
applications, such as urban planning and autonomous driving. While much
progress has been made in interpreting SS predictions for images, interpreting
point cloud SS predictions remains a challenge. This paper introduces pGS-CAM,
a novel gradient-based method for generating saliency maps in neural network
activation layers. Inspired by Grad-CAM, which uses gradients to highlight
local importance, pGS-CAM is robust and effective on a variety of datasets
(SemanticKITTI, Paris-Lille3D, DALES) and 3D deep learning architectures
(KPConv, RandLANet). Our experiments show that pGS-CAM effectively accentuates
the feature learning in intermediate activations of SS architectures by
highlighting the contribution of each point. This allows us to better
understand how SS models make their predictions and identify potential areas
for improvement. Relevant codes are available at
https://github.com/geoai4cities/pGS-CAM.
