[1]蒋腾平,王永君,陶帅兵,等.功能性规则约束下的三维点云道路设施语义标注[J].南京师范大学学报(工程技术版),2018,(04):052.[doi:10.3969/j.issn.1672-1292.2018.04.008]
 Jiang Tengping,Wang Yongjun,Tao Shuaibing,et al.3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules[J].Journal of Nanjing Normal University(Engineering and Technology),2018,(04):052.[doi:10.3969/j.issn.1672-1292.2018.04.008]
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功能性规则约束下的三维点云道路设施语义标注
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年04期
页码:
052
栏目:
计算机工程
出版日期:
2018-12-30

文章信息/Info

Title:
3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules
文章编号:
1672-1292(2018)04-0052-07
作者:
蒋腾平12王永君12陶帅兵12李云莉12刘 姗3
(1.南京师范大学虚拟地理环境教育部重点实验室,江苏 南京 210023)(2.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023)(3.厦门大学海洋与地球学院,福建 厦门 361102)
Author(s):
Jiang Tengping12Wang Yongjun12Tao Shuaibing12Li Yunli12Liu Shan3
(1.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)(3.College of Ocean and Earth Science,Xiamen University,Xiamen 361102,China)
关键词:
道路设施解译路灯交通标志移动激光扫描点云
Keywords:
road facility interpretationstreet lightstraffic signmobile laser scanningpoint cloud
分类号:
P237
DOI:
10.3969/j.issn.1672-1292.2018.04.008
文献标志码:
A
摘要:
三维场景的语义标注研究是机器视觉、摄影测量以及机器学习等领域的热门研究课题. 但基于移动激光扫描数据的道路设施精确解释仍处于瓶颈期. 提出一种基于逻辑关系和功能性对道路设施进行语义标注的新方法,先总结制定道路设施相关的特征符号和规则,再根据所定义的规则功能对点云数据进行语义标注. 基于该方法对国内某中等城市道路点云数据进行了相当详尽的解释,正确提取了93%的杆状物体,并全部正确识别. 对于杆状物体的附件(如灯头、交通标志等),基本正确识别且有效标记. 与改进的RANSAC算法相比,该方法提供了一个较好的解决方案,有助于在城市环境中自动绘制详细的道路设施.
Abstract:
Nowadays 3D scene labeling has become a hot topic in the reform of machine learning,photogrammerty,computer vision,etc. Road facility semantic labelling is vital for large scale mapping and autonomous driving systems. This paper proposes a new method for semantic tagging of road facilities based on logical relationships and functionality. We first summarize the relevant feature symbols and rules for road facilities,and then we semantically tag the point cloud data according to the defined rules. Based on the method proposed by us,a detailed explanation of road point cloud data in a medium-sized city in China is conducted. 93% of rod-shaped objects are correctly extracted and correctly identified. For attachments to rod-shaped objects(such as lamp heads,traffic signs,etc.),they are basically correctly identified and effectively marked. Compared with the improved RANSAC algorithm,our framework provides a more promising solution to automatically map road furniture at a detailed level in urban environments.

参考文献/References:

[1] VOSSELMAN G,GORTE B G,RABBANI T. Recognising structure in laser scanner point clouds[J]. International archives of photogrammetry,remote sensing and spatial information sciences,2004,46(8/wz):33-38.
[2]FISCHLER M A,BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM,1981,24(6):381-395.
[3]SCHNABEL R,WAHL R,KLEIN R. Efficient RANSAC for point cloud shape detection[J]. Computer graph forum,2010,26(2):214-226.
[4]TARSHA K F,LANDES T,GRUSSENMEYER P. Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from LiDAR data[C]//ISPRS Workshop on Laser Scanning 2007 and Silvi Laser 2007. Espoo,Finland,2007.
[5]PU S,RUTZINGER M,VOSSELMAN G,et al. Recognizing basic structures from mobile laser scanning data for road inventory studies[J]. ISPRS journal of photogrammetry and remote sensing,2011,66(6):S28-S39.
[6]RIVEIRO B,DíAZ-VILARI?O L,CONDECARNERO B,et al. Automatic segmentation and shape-based classification of retro-reflective traffic signs from mobile LiDAR data[J]. IEEE journal of selected topics in applied earth observations and remote sensing,2016,9(1):295-303.
[7]LI D,ELBERINK S D. Optimizing detection of road furniture(pole-like objects)in mobile laser scanner data[J]. ISPRS annals of photogrammetry,remote sensing and spatial information sciences,2013,1:163-168.
[8]EL-HALAWANY S I,LICHTI D D. Detecting road poles from mobile terrestrial laser scanning data[J]. Mapping sciences and remote sensing,2013,50(6):704-722.
[9]OUDE E S,KEMBOI B. User-assisted object detection by segment based similarity measures in mobile laser scanner data[C]//ISPRS Technical Commission Ⅲ Symposium. Zurich,Switzerland,2014.
[10]CABO C,ORDO?EZ C,GARCíA-CORTéS S,et al. An algorithm for automatic detection of pole-like street furniture objects from mobile laser scanner point clouds[J]. ISPRS journal of photogrammetry and remote sensing,2014,87(1):47-56.
[11]YANG B,DONG Z,ZHAO G,et al. Hierarchical extraction of urban objects from mobile laser scanning data[J]. ISPRS journal of photogrammetry and remote sensing,2015,99:45-57.
[12]GOLOVINSKIY A,KIM V G,FUNKHOUSER T. Shape-based recognition of 3D point clouds in urban environments[C]//IEEE 12th International Conference on Computer Vision. Kyoto,Japan,2009.
[13]YU Y,LI J,GUAN H,et al. Semi-automated extraction of street light poles from mobile LiDAR point-clouds[J]. IEEE transactions on geoscience and remote sensing,2015,53(3):1374-1386.
[14]MUNOZ D,BAGNELL J A,VANDAPEL N,et al. Contextual classification with functional max-margin Markov networks[C]//IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA,2009.
[15]ROSS S,MUNOZ D,HEBERT M,et al. Learning message-passing inference machines for structured prediction[C]//IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs,USA,2011.
[16]XIONG X,MUNOZ D,BAGNELL J A,et al. 3-D scene analysis via sequenced predictions over points and regions[C]//IEEE International Conference on Robotics and Automation(ICRA). Shanghai,China,2011.
[17]VELIZHEV A,SHAPOVALOV R,SCHINDLER K. Implicit shape models for object detection in 3D point clouds[J]. ISPRS annals of the photogrammetry,remote sensing and spatial information sciences,2012,3:179-184.
[18]KANAI S. Detection and classification of pole-like objects from mobile laser scanning data of urban environments[J]. International journal of CAD/CAM,2013,13(2):31-40.
[19]YANG B,DONG Z. A shape-based segmentation method for mobile laser scanning point clouds[J]. ISPRS journal of photogrammetry and remote sensing,2013,81(7):19-30.
[20]HUANG J,YOU S. Pole-like object detection and classification from urban point clouds[C]//IEEE International Conference on Robotics and Automation(ICRA). Seattle,USA,2015.
[21]SOILáN M,RIVEIRO B,MARTíNEZ-SáNCHEZ J,et al. Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory[J]. ISPRS journal of photogrammetry and remote sensing,2016,114:92-101.
[22]LEHTOM?KI M,JAAKKOLA A,HYYPP? J,et al. Object classification and recognition from mobile laser scanning point clouds in a road environment[J]. IEEE transactions on geoscience and remote sensing,2016,54(2):1226-1239.
[23]FUKANO K,MASUDA H. Detection and Classification of pole-like objects from mobile mapping data[J]. ISPRS annals of photogrammetry,remote sensing and spatial information sciences,2015,2:57-64.
[24]HACKEL T,WEGNER J D,SCHINDLER K,et al. Fast semantic segmentation of 3D point clouds with strongly varying density[J]. ISPRS annals of the photogrammetry,remote sensing and spatial information sciences,2016,3(3):177-184.
[25]WEINMANN M,JUTZI B,HINZ S,et al. Semantic point cloud interpretation based on optimal neighborhoods,relevant features and efficient classifiers[J]. ISPRS journal of photogrammetry and remote sensing,2015,105:286-304.
[26]TOMBARI F,FIORAIO N,CAVALLARI T,et al. Automatic detection of pole-like structures in 3d urban environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago,USA,2014.
[27]SONG S,XIAO J. Sliding shapes for 3d object detection in depth images[C]//European Conference on Computer Vision. Zurich,Switzerland,2014.
[28]WU Z,SONG S,KHOSLA A,et al. 3D ShapeNets:a deep representation for volumetric shapes[C]//IEEE Conference on Computer Vision and Pattern Recognition. Columbus,USA,2015.
[29]VOSSELMAN G. Point cloud segmentation for urban scene classification[J]. ISPRS international archives of photogrammetry,remote sensing and spatial information sciences,2013,1:257-262.

备注/Memo

备注/Memo:
收稿日期:2018-04-26.
基金项目:国家自然科学基金(41771439)、国家重点研发计划项目(2016YFB0502300)、江苏省研究生科研与实践创新计划项目(KYCX18_1206).
通讯联系人:王永君,博士,副教授,研究方向:三维GIS. E-mail:wangyongjun@njnu.edu.cn
更新日期/Last Update: 2018-12-30