RailPC: A large-scale railway point cloud semantic segmentation dataset  

作　　者：Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang 

机构地区：[1]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing Normal University,Nanjing,China [2]Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation,Ministry of Natural Resources,Nanjing,China [3]Eastern Institute of Technology(EIT),Ningbo,China [4]Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology,Tianjin,China [5]College of Computer Science and Engineering,Northwest Normal University,Lanzhou,China [6]Mckelvey School of Engineering,Washington University in St.Louis,St.Louis,Missouri,USA [7]School of Electronic and Information Engineering,Ningbo University of Technology,Ningbo,China

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1548-1560,共13页智能技术学报(英文)

基　　金：Key Laboratory of Degraded and Unused Land Consolidation Engineering,Ministry of Natural Resources of China,Grant/Award Number:SXDJ2024-22;Technology Innovation Centre for Integrated Applications in Remote Sensing and Navigation,Ministry of Natural Resources of China,Grant/Award Number:TICIARSN-2023-06;National Natural Science Foundation of China,Grant/Award Numbers:42171446,62302246;Zhejiang Provincial Natural Science Foundation of China,Grant/Award Number:LQ23F010008;Science and Technology Program of Tianjin,China,Grant/Award Number:23ZGSSSS00010。

摘　　要：Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific datasets.Additionally,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed etc.To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud benchmark.RailPC provides a large-scale dataset with rich annotations for semantic segmentation in the railway environment.Notably,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic segmentation.It covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS systems.Through extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results.Based on our findings,we establish some critical challenges towards railway-scale point cloud semantic segmentation.The dataset is available at https://github.com/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关 键 词：data benchmark MLS point clouds railway scene semantic segmentation 

分 类 号：F53[经济管理—产业经济]