HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point clouds

TL;DR

This paper introduces HRHD-HK, a comprehensive benchmark dataset of high-rise, high-density urban photogrammetric point clouds from Hong Kong, enabling better evaluation and development of 3D semantic segmentation methods.

Contribution

It provides the first photogrammetric dataset focused on high-rise, high-density urban scenes, and evaluates existing segmentation methods on this challenging dataset.

Findings

Current segmentation methods have significant room for improvement.

Small-volume objects are particularly challenging for existing methods.

The dataset enables comprehensive assessment of urban 3D segmentation techniques.

Abstract

Many existing 3D semantic segmentation methods, deep learning in computer vision notably, claimed to achieve desired results on urban point clouds. 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). 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 our best 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://doi.org/10.25442/hku.23701866.v2.