Many existing 3D semantic segmentation methods, deep learning in computer vision notably, claimed to achieve desired results on urban point clouds, in which the city objects are too many and diverse for people to judge qualitatively. Thus, it is significant to assess these methods quantitatively in diversified real-world urban scenes, encompassing high-rise, low-rise, high-density, and low-density urban areas. However, existing public benchmark datasets primarily represent low-rise scenes from European cities and cannot assess the methods comprehensively. This paper presents a benchmark dataset of high-rise urban point clouds, namely High-Rise, High-Density urban scenes of Hong Kong (HRHD-HK), which has been vacant for a long time. HRHD-HK arranged in 150 tiles contains 273 million colorful photogrammetric 3D points from diverse urban settings. The semantic labels of HRHD-HK include building, vegetation, road, waterbody, facility, terrain, and vehicle. To the best of our knowledge, HRHD-HK is the first photogrammetric dataset that focuses on HRHD urban areas. This paper also comprehensively evaluates eight popular semantic segmentation methods on the HRHD-HK dataset. Experimental results confirmed plenty of room for enhancing the current 3D semantic segmentation of point clouds, especially for city objects with small volumes. Our dataset is publicly available at: https://github.com/LuZaiJiaoXiaL/HRHD-HK.

本文提出一种高层、高密度城市点云基准数据集(HRHD-HK)，同时对现有的8种语义分割算法在此数据集上进行了全面评估，实验结果表明目前的点云3D语义分割仍有很大提升空间，特别是对于小体积的城市对象来说。

HRHD-HK：高层和高密度城市场景的基准数据集，用于摄影测量点云的3D语义分割