Feature Line Generation and Regularization From Point Clouds

The shape of the object is mainly described by feature points and lines. Since a feature point can be described by the intersection of two feature lines, feature lines are the key to determine the contour of the object. In this article, a novel method for the generation and regularization of point cloud feature line is presented, which consists of two main steps: extraction of the outline points according to the property of vectors distribution and cluster, feature points are sorted according to the vector deflection angle and distance and they are fitted using the improved cubic b-spline curve fitting algorithm. The performance of the proposed method is evaluated with both large and small point clouds acquired by terrestrial laser scanning devices in real-world scenes. The results show that the proposed method and the analysis of geometrical properties of neighborhoods (AGPN) method achieve very similar performance in the case of planar objects, accurately extracting the outline points of objects. However, in the presence of a curved surface, the proposed method significantly outperforms the existing methods in detecting outline points. The outlines are regularized by the improved cubic b-spline and it is superior to the traditional cubic b-spline curve fitting algorithm.

[1]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[2]  R. Cochran,et al.  Statistically weighted principal component analysis of rapid scanning wavelength kinetics experiments , 1977 .

[3]  Werner Frei,et al.  Fast Boundary Detection: A Generalization and a New Algorithm , 1977, IEEE Transactions on Computers.

[4]  Ferdinand van der Heijden,et al.  Edge and Line Feature Extraction Based on Covariance Models , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Stefan Gumhold,et al.  Feature Extraction From Point Clouds , 2001, IMR.

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

[7]  Wolfgang Förstner,et al.  Matching, reconstructing and grouping 3D lines from multiple views using uncertain projective geometry , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  S. Dudoit,et al.  A prediction-based resampling method for estimating the number of clusters in a dataset , 2002, Genome Biology.

[10]  André Meyer,et al.  Segmentation of 3D triangulated data points using edges constructed with a C1 discontinuous surface fitting , 2004, Comput. Aided Des..

[11]  Dirk Roose,et al.  Detection of closed sharp edges in point clouds using normal estimation and graph theory , 2007, Comput. Aided Des..

[12]  Amy V Kapp,et al.  Are clusters found in one dataset present in another dataset? , 2007, Biostatistics.

[13]  M. Gross,et al.  Algebraic point set surfaces , 2007, SIGGRAPH 2007.

[14]  Sukhan Lee,et al.  Robust 3D Line Extraction from Stereo Point Clouds , 2008, 2008 IEEE Conference on Robotics, Automation and Mechatronics.

[15]  Hans-Peter Seidel,et al.  Exploiting global connectivity constraints for reconstruction of 3D line segments from images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Qing Wang,et al.  3D Line Segment Detection for Unorganized Point Clouds from Multi-view Stereo , 2010, ACCV.

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

[18]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Michael Bosse,et al.  Vision-based localization using an edge map extracted from 3D laser range data , 2010, 2010 IEEE International Conference on Robotics and Automation.

[20]  Leonidas J. Guibas,et al.  Voronoi-Based Curvature and Feature Estimation from Point Clouds , 2011, IEEE Transactions on Visualization and Computer Graphics.

[21]  Hai Huang,et al.  A HYBRID APPROACH TO EXTRACTION AND REFINEMENT OF BUILDING FOOTPRINTS FROM AIRBORNE LIDAR DATA , 2011 .

[22]  Cuneyt Akinlar,et al.  EDLines: A real-time line segment detector with a false detection control , 2011, Pattern Recognit. Lett..

[23]  Niloy J. Mitra,et al.  Factored Facade Acquisition using Symmetric Line Arrangements , 2012, Comput. Graph. Forum.

[24]  Jiang Jian-sheng Improved algorithm for extraction of boundary characteristic point from scattered point cloud , 2012 .

[25]  Soo-Kyun Kim Extraction of ridge and valley lines from unorganized points , 2012, Multimedia Tools and Applications.

[26]  Bisheng Yang,et al.  Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds , 2013 .

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

[28]  Xiangyun Hu,et al.  A FAST AND SIMPLE METHOD OF BUILDING DETECTION FROM LIDAR DATA BASED ON SCAN LINE ANALYSIS , 2013 .

[29]  Michael Bosse,et al.  Line-based extrinsic calibration of range and image sensors , 2013, 2013 IEEE International Conference on Robotics and Automation.

[30]  Enkhbayar Altantsetseg,et al.  Feature line extraction from unorganized noisy point clouds using truncated Fourier series , 2013, The Visual Computer.

[31]  Cheng Wang,et al.  Line segment extraction for large scale unorganized point clouds , 2015 .

[32]  Mohammad Awrangjeb,et al.  Using point cloud data to identify, trace, and regularize the outlines of buildings , 2016 .

[33]  Xiangguo Lin,et al.  Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods , 2016, Remote. Sens..

[34]  Shanshan Li,et al.  A statistical approach for extraction of feature lines from point clouds , 2016, Comput. Graph..