# Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization

Source: [Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization](<https://www.sota2.com/research/paper/68fa72411e209131cea554ef>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:30:07.638Z

Authors: Abhishek Kuriyal, Vaibhav Kumar

Published: 2024-02-19T12:27:39.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.12098>)

[PDF](<https://arxiv.org/pdf/2402.12098.pdf>)