A Methodology for Automated Segmentation and Reconstruction of Urban 3-D Buildings from ALS Point Clouds

In this paper, a methodology which allows automated and efficient reconstruction of three-dimensional (3-D) geometric building models from an Airborne Laser Scanning (ALS) point cloud is introduced and its performance is analyzed and evaluated. The proposed method avoids abnormal and/or infinite solutions which are typically encountered in previously published methods that use the rooftop primitive adjacency matrix to solve the critical rooftop vertices. In particular, first, an improved random sample consensus (RANSAC) algorithm is proposed to segment the rooftop primitives, i.e., the planar patches that constitute rooftops, of each building or group of connected buildings. The algorithm successfully maintains topological consistency among primitives and avoids under- and over-segmentation with high efficiency. Second, a novel Voronoi-based primitive boundary extraction algorithm under constraints of outer and inner building boundaries is introduced in order to extract each primitive boundary. In this algorithm, the adjacent segmented primitive relationships among the various primitives are preserved by a subgraph of the Voronoi diagram so that the reconstructed neighbor primitives are seamlessly connected. Third, in order to refine the boundary shapes of primitives with irregular geometry, various criteria for making the boundary adjustments more effective are proposed. In this way, more regular 3-D buildings can be produced. Finally, the primitive boundary simplification criteria are formally introduced to generate compact 3-D building models. By using the simplification criteria, nonadjacency between neighbor primitives, intersection between boundaries, and self-intersections are, to a great extent, avoided. Numerous experimental results obtained using multiple data sets, including data from the cities of Toronto and Enschede as well as from the Niagara area, have shown that the proposed methodology has excellent performance and it can produce watertight 3-D polyhedral building models.

[1]  R.-J. You,et al.  A Quality Prediction Method for Building Model Reconstruction Using LiDAR Data and Topographic Maps , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Ulrich Neumann,et al.  2.5D Dual Contouring: A Robust Approach to Creating Building Models from Aerial LiDAR Point Clouds , 2010, ECCV.

[3]  Zhang Liqiang,et al.  A spatial cognition-based urban building clustering approach and its applications , 2013 .

[4]  Fan Zhang,et al.  Classification of airborne laser scanning data using JointBoost , 2015 .

[5]  Ulrich Neumann,et al.  2.5D building modeling by discovering global regularities , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  J. Shan,et al.  BUILDING ROOF SEGMENTATION AND RECONSTRUCTION FROM LIDAR POINT CLOUDS USING CLUSTERING TECHNIQUES , 2008 .

[7]  Shaohui Sun,et al.  Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Jarek Rossignac,et al.  Multi-resolution 3D approximations for rendering complex scenes , 1993, Modeling in Computer Graphics.

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

[10]  Andrew Zisserman,et al.  New Techniques for Automated Architectural Reconstruction from Photographs , 2002, ECCV.

[11]  Charalambos Poullis,et al.  A Framework for Automatic Modeling from Point Cloud Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  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.

[13]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[14]  Chun Liu,et al.  Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Norbert Haala,et al.  An update on automatic 3D building reconstruction , 2010 .

[16]  Ling Yang,et al.  Web-based terrain and vector maps visualization for Wenchuan earthquake , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Reinhard Klein,et al.  OUT-OF-CORE TOPOLOGICALLY CONSTRAINED SIMPLIFICATION FOR CITY MODELING FROM DIGITAL SURFACE MODELS , 2009 .

[18]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[19]  Zhen Wang,et al.  A spatial cognition-based urban building clustering approach and its applications , 2013, Int. J. Geogr. Inf. Sci..

[20]  Jonathan Richard Shewchuk,et al.  Delaunay refinement algorithms for triangular mesh generation , 2002, Comput. Geom..

[21]  Suya You,et al.  Photorealistic Large-Scale Urban City Model Reconstruction , 2009, IEEE Transactions on Visualization and Computer Graphics.

[22]  Clive S. Fraser,et al.  Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs , 2014, Remote. Sens..

[23]  Rey-Jer You,et al.  Building Feature Extraction from Airborne Lidar Data Based on Tensor Voting Algorithm , 2011 .

[24]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[25]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[26]  D. Fritsch,et al.  AUTOMATIC 3D BUILDING RECONSTRUCTION USING PLANE-ROOF STRUCTURES , 2000 .

[27]  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).

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

[29]  I. Dowman,et al.  Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction * , 2007 .

[30]  Jinfei Wang,et al.  An Evaluation System for Building Footprint Extraction From Remotely Sensed Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Jie Shan,et al.  Building roof modeling from airborne laser scanning data based on level set approach , 2011 .

[32]  R. Klein,et al.  From Detailed Digital Surface Models to City Models Using Constrained Simplification , 2008 .

[33]  Clive S. Fraser,et al.  An Automatic and Threshold-Free Performance Evaluation System for Building Extraction Techniques From Airborne LIDAR Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Jianxiong Xiao,et al.  Image-based façade modeling , 2008, ACM Trans. Graph..

[35]  C. Fraser,et al.  AUTOMATIC RECONSTRUCTION OF BUILDING ROOFS THROUGH EFFECTIVE INTEGRATION OF LIDAR AND MULTISPECTRAL IMAGERY , 2012 .

[36]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[37]  Clive S. Fraser,et al.  RULE-BASED SEGMENTATION OF LIDAR POINT CLOUD FOR AUTOMATIC EXTRACTION OF BUILDING ROOF PLANES , 2013 .

[38]  William E. Lorensen,et al.  Decimation of triangle meshes , 1992, SIGGRAPH.

[39]  G. Sohn,et al.  Using a Binary Space Partitioning Tree for Reconstructing Polyhedral Building Models from Airborne Lidar Data , 2008 .

[40]  Jan-Michael Frahm,et al.  Towards Urban 3D Reconstruction from Video , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[41]  Qian-Yi Zhou,et al.  Fast and extensible building modeling from airborne LiDAR data , 2008, GIS '08.

[42]  Josiane Zerubia,et al.  Structural Approach for Building Reconstruction from a Single DSM , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Uwe Soergel,et al.  Relevance assessment of full-waveform lidar data for urban area classification , 2011 .

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

[45]  Daniel G. Aliaga,et al.  A Survey of Urban Reconstruction , 2013, Comput. Graph. Forum.

[46]  George Vosselman,et al.  Quality analysis on 3D building models reconstructed from airborne laser scanning data , 2011 .

[47]  Zhen Wang,et al.  A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs , 2013, Science China Information Sciences.

[48]  Claus Brenner,et al.  Generation Of 3D City Models From Airborne Laser Scanning Data , 1997 .

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

[50]  Hao Wang,et al.  Perception-based shape retrieval for 3D building models , 2013 .

[51]  Markus Hollaus,et al.  Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data , 2012 .

[52]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[53]  Norbert Pfeifer,et al.  A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[54]  Francis Schmitt,et al.  Mesh Simplification , 1996, Comput. Graph. Forum.

[55]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[56]  C. Fraser,et al.  Automatic extraction of building roofs using LIDAR data and multispectral imagery , 2013 .