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Point cloud benchmark dataset WHU-TLS and WHU-MLS for deep learning

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China

  • Email：bshyang@whu.edu.cn
  • Introduction：杨必胜， 1974年生，男，教授，研究方向为无人机摄影测量与三维重建、点云智能处理、空间智能、GIS应用。E-mail：bshyang@whu.edu.cn
YANG Bisheng12,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China

HAN Xu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China

  • Email：dongzhenwhu@whu.edu.cn
  • Introduction：董震，1988年生，男，副研究员，研究方向为点云智能处理、3D计算机视觉。E-mail：dongzhenwhu@whu.edu.cn
DONG Zhen12*

Resümee

This paper aims to elaborate two large-scale point cloud benchmark datasets, namely, WHU-TLS and WHU-MLS, for deep learning purposes. The benchmark of the Whu-TLS data set comprises 115 scans and over 1740 million 3D points collected from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. The aims of the proposed benchmark are to facilitate better comparisons and provide insights into the strengths and weaknesses of different registration approaches based on a common standard.The ground-truth transformations and registration graphs are also provided to allow researchers to evaluate their registration solutions and for environmental modeling. In addition, the Whu-TLS data set provides suitable data for applications in safe railway operation, river surveys and regulation, forest structure assessment, cultural heritage conservation, landslide monitoring, and underground asset management. WHU-MLS benchmark dataset includes more than 30 kinds of objects and 5000 typical instances in urban scene. We manually labeled MLS point cloud, each point with spatial coordinates and normal. We totally labeled 40 scenes with average number of points 8 million, of which 30 scenes are split for training and 10 scenes for testing.The coarse and fine categories are defined as follows. The Construction: building (including the building façade and other clutters in the building), fence (including isolation structure on the road and wall); Natural: trees, low vegetation, including grass, shrub and other low tree; Ground: driveway (not including road mark), non-drive way, the ground that does not belong to the driveway, road markings; Dynamic: person (including person and bikes), car; Pole: light, electric pole, municipal pole, signal light, detector, board (usually attached to the light). The semantic labeling and instance labeling in WHU-MLS provide important references for point cloud deep learning. On the one hand, these datasets can be used for point cloud deep learning networks the training, testing, and evaluation of point cloud deep learning networks. On the other hand, the benchmark datasets would can promote the benchmarking of state-of-the-art algorithms in this field, and ensure better comparisons on a common base. WHU-TLS and WHU-MLS are freely available can be used freely for scientific research. We hope that the Whu-TLS and Whu-MLS benchmark data sets meet the needs of the research community and becomes important data sets for the development of cutting-edge TLS point cloud registration and point cloud segmentation methods.

Schlüsselwort

remote sensing;deep learning;registration;semantic segmentation;instance segmentation;benchmark

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