RailCloud-HdF: A Large-Scale Point Cloud Dataset for Railway Scene Semantic Segmentation

Abstract

Semantic scene perception is critical for various applications, including railway systems where safety and efficiency are paramount. Railway applications demand precise knowledge of the environment, making Light Detection and Ranging (LiDAR) a fundamental component of sensor suites. Despite the significance of 3D semantic scene understanding in railway context, there exists no publicly available railborne LiDAR dataset tailored for this purpose. In this work, we present a large-scale point cloud dataset designed to advance research in LiDAR-based semantic scene segmentation for railway applications. Our dataset offers dense point-wise annotations for diverse railway scenes, covering over 267km. To facilitate rigorous evaluation and benchmarking, we propose semantic segmentation of point clouds from a single LiDAR scan as a challenging task. Furthermore, we provide baseline experiments to showcase some state-of-the-art deep learning methods for this task. Our findings highlight the need for more advanced models to effectively address this task. This dataset not only catalyzes the development of sophisticated methods for railway applications, but also encourages exploration of novel research directions.