|Table of Contents|

3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2018年04期
Page:
52-
Research Field:
计算机工程
Publishing date:

Info

Title:
3D Point Cloud Road Facilities Semantic LabelingConstrained by Functional Rules
Author(s):
Jiang Tengping1; 2; Wang Yongjun1; 2; Tao Shuaibing1; 2; Li Yunli1; 2; Liu 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)
Keywords:
road facility interpretation; street lights; traffic sign; mobile laser scanning; point cloud
PACS:
P237
DOI:
10.3969/j.issn.1672-1292.2018.04.008
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.

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.

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Last Update: 2018-12-30