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Received：10 October 2020，
Published：07 January 2021
移动端阅览
为推进深度学习方法在点云配准、语义分割、实例分割等领域的发展，武汉大学联合国内外多家高等院校和研究机构发布了包含多类型场景的地面站点云配准基准数据集WHU-TLS和包含语义、实例的城市级车载点云基准数据集WHU-MLS。其中，WHU-TLS基准数据集涵盖了地铁站、高铁站、山地、公园、校园、住宅、河岸、文化遗产建筑、地下矿道、隧道等10种不同的环境，共包含115个测站、17.4亿个三维点以及点云之间的真实转换矩阵，为点云配准提供了迄今为止最大规模的基准数据集。WHU-MLS基准数据集涵盖了地面特征（机动车道、道路标线、井盖、非机动车道），动态目标（行人、车辆），植被（树木、树丛、低矮植被），杆状地物及其附属结构（电线杆、独立提示牌、路灯、信号灯、独立探头等），建筑和结构设施（房屋、道路隔离结构、围墙和栅栏）以及其他公共和便利设施（垃圾桶、邮筒、消防栓、街头座椅、电力线等）等6大类30余小类地物要素，共包含2亿多个点和超过5000个实例对象，为语义分割、实例分割点云深度学习网络的训练、测试和性能评估提供了当前最为丰富的基准数据集。
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.
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