AN ADAPTIVE APPROACH FOR SEGMENTATION OF 3D LASER POINT CLOUD

Abstract. Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.

[1]  José Luis Lerma,et al.  Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods , 2008 .

[2]  S. Filin.  SURFACE CLUSTERING FROM AIRBORNE LASER SCANNING DATA , 2002 .

[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]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

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

[6]  N. Pfeifer,et al.  SEGMENTATION BASED ROBUST INTERPOLATION - A NEW APPROACH TO LASER DATA FILTERING , 2005 .

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

[8]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[9]  Shi Pu,et al.  AUTOMATIC EXTRACTION OF BUILDING FEATURES FROM TERRESTRIAL LASER SCANNING , 2006 .

[10]  George Vosselman,et al.  Automatic extraction of building features from terrestrial laser scanning , 2006 .

[11]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[12]  Changjae Kim,et al.  NEW APPROACH FOR PLANAR PATCH SEGMENTATION USING AIRBORNE LASER DATA , 2007 .

[13]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[15]  Norbert Pfeifer,et al.  New Associate Editor pp iii-iv Segmentation of airborne laser scanning data using a slope adaptive neighborhood , 2006 .

[16]  N. Pfeifer,et al.  Neighborhood systems for airborne laser data , 2005 .