WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation

Point cloud semantic segmentation (PCSS) shows great potential in generating accurate 3D semantic maps for digital twin railways. Deep learning-based methods have seen substantial advancements, driven by numerous PCSS datasets. Nevertheless, existing datasets tend to neglect railway scenes, with limitations in scale, categories, and scene diversity. This motivated us to establish WHU-Railway3D, a diverse PCSS dataset specifically designed for railway scenes. WHU-Railway3D is categorized into urban, rural, and plateau railways based on scene complexity and semantic class distribution. The dataset spans approximately 30 km with 4.6 billion points labeled into 11 classes, such as rails, masts, overhead lines, and fences. In addition to 3D coordinates, WHU-Railway3D provides rich attribute information such as reflected intensity, scanning angle, and number of returns. Cutting-edge methods are extensively evaluated on the dataset, followed by in-depth analysis. Lastly, key challenges and potential future work are identified to stimulate further innovative research. The dataset is accessible at https://github.com/WHU-USI3DV/WHU-Railway3D.