Segmentation of LiDAR Data Using Multilevel Cube Code

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

[1]  George Vosselman,et al.  3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS , 2001 .

[2]  Q. Guo,et al.  Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods , 2010 .

[3]  Chi-Kuei Wang,et al.  Effect of point density and interpolation of LiDAR-derived high-resolution DEMs on landscape scarp identification , 2014 .

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

[5]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[6]  Hiram H. López-Valdez,et al.  A new relative chain code in 3D , 2014, Pattern Recognit..

[7]  Xiangguo Lin,et al.  SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas , 2013, Remote. Sens..

[8]  Syed Ali Naqi Gilani,et al.  Segmentation of Airborne Point Cloud Data for Automatic Building Roof Extraction , 2018 .

[9]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[10]  Park,et al.  Correction of Erroneous Model Key Points Extracted from Segmented Laser Scanner Data and Accuracy Evaluation , 2013 .

[11]  A. Amr,et al.  Geomatics 3 D model reconstruction from aerial ortho-imagery and LiDAR data , 2017 .

[12]  T. Schenk,et al.  Fusing Imagery and 3D Point Clouds for Reconstructing Visible Surfaces of Urban Scenes , 2007, 2007 Urban Remote Sensing Joint Event.

[13]  J. Shan,et al.  Building boundary tracing and regularization from airborne lidar point clouds , 2007 .

[14]  Ayman Habib,et al.  INTERPOLATION OF LIDAR DATA AND AUTOMATIC BUILDING EXTRACTION , 2002 .

[15]  G. Sohn,et al.  Extraction of buildings from high-resolution satellite data and airborne Lidar , 2000 .

[16]  Yuan Zhang,et al.  Urban building reconstruction from raw LiDAR point data , 2017, Comput. Aided Des..

[17]  C. Briese,et al.  AUTOMATIC GENERATION OF BUILDING MODELS FROM LIDAR DATA AND THE INTEGRATION OF AERIAL IMAGES , 2003 .

[18]  C. Brenner,et al.  A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data , 2013 .

[19]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[20]  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.

[21]  Robert J. Schalkoff,et al.  Digital Image Processing and Computer Vision , 1989 .

[22]  George Miliaresis,et al.  Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs , 2007, Comput. Geosci..

[23]  Dong Chen,et al.  Topologically Aware Building Rooftop Reconstruction From Airborne Laser Scanning Point Clouds , 2017, IEEE Transactions on Geoscience and Remote Sensing.

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