Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans

• URL: http://arxiv.org/abs/2504.21602v1
• Date: Wed, 30 Apr 2025 13:00:50 GMT
• Title: Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
• Authors: Hannes Reichert, Benjamin Serfling, Elijah Schüssler, Kerim Turacan, Konrad Doll, Bernhard Sick,
• Abstract summary: This study introduces a novel semantic segmentation framework tailored for modern high-resolution LiDAR sensors.<n>We propose a novel LiDAR dataset collected by a cutting-edge automotive 128 layer LiDAR in urban traffic scenes.<n>Our approach is bridging the gap between cutting-edge research and practical automotive applications.
• Score: 1.6093159644587223
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 However, many state-of-the-art methods are tested on outdated, lower-resolution LiDAR sensors and struggle with real-time constraints.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.

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