3D point cloud segmentation: A survey

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation. Many authors have introduced different approaches and algorithms. In this survey, we examine methods that have been proposed to segment 3D point clouds. The advantages, disadvantages, and design mechanisms of these methods are analyzed and discussed. Finally, we outline the promising future research directions.

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

[2]  Michel Devy,et al.  Fast range image segmentation by an edge detection strategy , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

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

[4]  Bir Bhanu,et al.  RANGE DATA PROCESSING: REPRESENTATION OF SURFACES BY EDGES. , 1986 .

[5]  S. Sotoodeh.  OUTLIER DETECTION IN LASER SCANNER POINT CLOUDS , 2006 .

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

[7]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

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

[9]  Martial Hebert,et al.  3-D scene analysis via sequenced predictions over points and regions , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Sebastian Thrun,et al.  An Application of Markov Random Fields to Range Sensing , 2005, NIPS.

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

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Baining Guo,et al.  Pocket reflectometry , 2011, SIGGRAPH 2011.

[14]  Edwin Olson,et al.  Graph-based segmentation for colored 3D laser point clouds , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[16]  Ariel Shamir,et al.  Segmentation and Shape Extraction of 3D Boundary Meshes , 2006, Eurographics.

[17]  Thorsten Joachims,et al.  Contextually Guided Semantic Labeling and Search for 3D Point Clouds , 2011, ArXiv.

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

[19]  Jie Chen,et al.  Architectural Modeling from Sparsely Scanned Range Data , 2008, International Journal of Computer Vision.

[20]  Nico Blodow,et al.  Fast geometric point labeling using conditional random fields , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Horst Bunke,et al.  Fast range image segmentation using high-level segmentation primitives , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[22]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ben Taskar,et al.  Discriminative learning of Markov random fields for segmentation of 3D scan data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[26]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Thomas A. Funkhouser,et al.  Min-cut based segmentation of point clouds , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[28]  Abdul Nurunnabi,et al.  Robust Segmentation in Laser Scanning 3D Point Cloud Data , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[29]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[30]  Wolfram Burgard,et al.  Real-time 3D visual SLAM with a hand-held camera , 2011 .

[31]  Mark E. Campbell,et al.  Segmentation of dense range information in complex urban scenes , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[33]  Michael Spann,et al.  MIR: An Approach to Robust Clustering-Application to Range Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[35]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[36]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[37]  P. Dorninger,et al.  3 D SEGMENTATION OF UNSTRUCTURED POINT CLOUDS FOR BUILDING MODELLING , 2007 .

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

[39]  Jian S. Dai,et al.  2010 IEEE Conference on Robotics, Automation and Mechatronics , 2010 .

[40]  Leonidas J. Guibas,et al.  Shape segmentation using local slippage analysis , 2004, SGP '04.

[41]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[42]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[43]  Marc Jaeger,et al.  Segmentation of architecture shape information from 3D point cloud , 2009, VRCAI '09.

[44]  陈宝权.  GlobFit: Consistently Fitting Primitives by Discovering Global Relations , 2011 .

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