Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

2020-09-07 · CVPR 2021 1 · Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://github.com/QingyongHu/SensatUrban.

📄 PDF Abstract BibTeX arXiv:2009.03137

Code (2)

QingyongHu/SensatUrban 공식 구현 tf

QingyongHu/SQN tf

Tasks

Scene Understanding Semantic Segmentation

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

2020-03-18 · Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li 외

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments …

3D Semantic Segmentation Autonomous Driving Scene Understanding Segmentation+1

OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds

2025-09-13 · Chongyu Wang, Kunlei Jing, Jihua Zhu, Di Wang arxiv

Open-vocabulary semantic segmentation enables models to recognize and segment objects from arbitrary natural language descriptions, offering the flexibility to handle novel, fine-grained, or functionally defined categori…

Point Cloud Segmentation Semantic Segmentation Scene Understanding Point Clouds

Efficient Urban-scale Point Clouds Segmentation with BEV Projection

2021-09-19 · Zhenhong Zou, Yizhe Li

Point clouds analysis has grasped researchers' eyes in recent years, while 3D semantic segmentation remains a problem. Most deep point clouds models directly conduct learning on 3D point clouds, which will suffer from th…

3D Semantic Segmentation Segmentation Semantic Segmentation

Automatic labelling of urban point clouds using data fusion

2021-08-31 · Daan Bloembergen, Chris Eijgenstein

In this paper we describe an approach to semi-automatically create a labelled dataset for semantic segmentation of urban street-level point clouds. We use data fusion techniques using public data sources such as elevatio…

Management Segmentation Semantic Segmentation

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

2023-07-16 · Maosu Li, Yijie Wu, Anthony G. O. Yeh, Fan Xue

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 divers…