# Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans

Source: [Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans](<https://www.sota2.com/research/paper/68fa7b3f1e209131cea874a5>)

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

Data updated: 2025-10-26T05:35:42.317Z

Authors: Hannes Reichert, Benjamin Serfling, Elijah Sch\\"ussler, Kerim Turacan, Konrad Doll, Bernhard Sick

Published: 2025-04-30T13:00:50.000Z

## Abstract

In recent studies, numerous previous works emphasize the importance of semantic segmentation of LiDAR data as a critical component to the development of driver-assistance systems and autonomous vehicles. However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints. This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors that addresses both accuracy and real-time processing demands. We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes. Furthermore, we propose a semantic segmentation method utilizing surface normals as strong input features. Our approach is bridging the gap between cutting-edge research and practical automotive applications. Additionaly, we provide a Robot Operating System (ROS2) implementation that we operate on our research vehicle. Our dataset and code are publicly available: https://github.com/kav-institute/SemanticLiDAR.

## 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/2504.21602>)

[PDF](<https://arxiv.org/pdf/2504.21602.pdf>)