A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data

Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging) point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional pylon modeling (MD3DM) method using airborne LiDAR data. We start with constructing a parametric model of pylon, based on its actual structure and the characteristics of point clouds data. In this model, a pylon is divided into three parts: pylon legs, pylon body and pylon head. The modeling approach mainly consists of four steps. Firstly, point clouds of individual pylon are detected and segmented from massive high-voltage transmission corridor point clouds automatically. Secondly, an individual pylon is divided into three relatively simple parts in order to reconstruct different parts with different strategies. Its position and direction are extracted by contour analysis of the pylon body in this stage. Thirdly, the geometric features of the pylon head are extracted, from which the head type is derived with a SVM (Support Vector Machine) classifier. After that, the head is constructed by seeking corresponding model from pre-build model library. Finally, the body is modeled by fitting the point cloud to planes. Experiment results on several point clouds data sets from China Southern high-voltage nationwide transmission grid from Yunnan Province to Guangdong Province show that the proposed approach can achieve the goal of automatic three-dimensional modeling of the pylon effectively.

[1]  Robert A. McLaughlin,et al.  Extracting transmission lines from airborne LIDAR data , 2006, IEEE Geoscience and Remote Sensing Letters.

[2]  Norbert Pfeifer,et al.  A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds , 2008, Sensors.

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  Ulrich Neumann,et al.  A streaming framework for seamless building reconstruction from large-scale aerial LiDAR data , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  M. Jancosek,et al.  Flexible building primitives for 3D building modeling , 2015 .

[6]  E. Kjems,et al.  Automatic 3D building reconstruction from airbornelaser scanning and cadastral data using hough transform , 2004 .

[7]  S. J. Oude Elberink,et al.  A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds , 2014 .

[8]  Martin Kada,et al.  3D BUILDING RECONSTRUCTION FROM LIDAR BASED ON A CELL DECOMPOSITION APPROACH , 2009 .

[9]  Florent Lafarge,et al.  Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation , 2012, International Journal of Computer Vision.

[10]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[11]  Gunho Sohn,et al.  3D CLASSIFICATION OF POWER-LINE SCENE FROM AIRBORNE LASER SCANNING DATA USING RANDOM FORESTS , 2010 .

[12]  Herbert Edelsbrunner,et al.  Three-dimensional alpha shapes , 1992, VVS.

[13]  Jie Shan,et al.  Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Yoonseok Jwa,et al.  AUTOMATIC 3D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA , 2009 .

[15]  Vivek Verma,et al.  3D Building Detection and Modeling from Aerial LIDAR Data , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  K. Rückert Analysis and Comparison , 2011, Arabic Medicine in China.

[17]  Martin Kada,et al.  3D Building Adjustment Using Planar Half-Space Regularities , 2014 .

[18]  Anqing You,et al.  Applications of LiDAR in patrolling electric-power lines , 2013, 2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE).

[19]  Florent Lafarge,et al.  Surface Reconstruction through Point Set Structuring , 2013, Comput. Graph. Forum.

[20]  Monika Sester,et al.  3D building roof reconstruction from point clouds via generative models , 2011, GIS.

[21]  F. Tarsha-Kurdi,et al.  EXTENDED RANSAC ALGORITHM FOR AUTOMATIC DETECTION OF BUILDING ROOF PLANES FROM LIDAR DATA , 2008 .

[22]  Yoonseok Jwa,et al.  AUTOMATIC POWERLINE SCENE CLASSIFICATION AND RECONSTRUCTION USING AIRBORNE LIDAR DATA , 2012 .

[23]  Lutz Plümer,et al.  Model driven reconstruction of roofs from sparse LIDAR point clouds , 2013 .

[24]  Florent Lafarge,et al.  Building large urban environments from unstructured point data , 2011, 2011 International Conference on Computer Vision.

[25]  F. Tarsha-Kurdi,et al.  Model-driven and data-driven approaches using LIDAR data: analysis and comparison , 2007 .

[26]  Livia Theriault,et al.  Advantages of Airborne Lidar Technology in Power Line Asset Management , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[27]  M. Kada,et al.  Feature-Driven 3D Building Modeling using Planar Halfspaces , 2013 .

[28]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[29]  Peng Guang-xiong Algorithms Study of Building Boundary Extraction and Normalization Based on LIDAR Data , 2008 .

[30]  S. J. Oude Elberink,et al.  Target graph matching for building reconstruction , 2009 .

[31]  George Vosselman,et al.  Two algorithms for extracting building models from raw laser altimetry data , 1999 .

[32]  F. Tarsha-Kurdi,et al.  Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from Lidar Data , 2007 .