Primitive Shape Extraction for Objects in Point Cloud

Shape plays an important role in object recognition and shape modeling. In this paper, a comprehensive method is proposed to analyze and implement crucial primitive shape extraction including line, plane, cylinder and sphere. Linear shape is extracted by the proposed alpha-shape based method, and an algorithm that robustly computes the boundary of holes from incomplete objects is presented. The plane primitive shape is extracted by analyzing neighboring point distribution and the parameters that define the primitive shape. Reasonable primitive shape features is extracted accurately for further shape modeling. Experimental results demonstrate that our method could deal with different objects efficiently in different shapes.

[1]  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.

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

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

[4]  T. Rabbani,et al.  EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS , 2005 .

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

[6]  Jin Liu,et al.  An adaptive approach for primitive shape extraction from point clouds , 2014 .

[7]  George Vosselman,et al.  Knowledge based reconstruction of building models from terrestrial laser scanning data , 2009 .

[8]  H. Maas,et al.  KNOWLEDGE-BASED BUILDING DETECTION BASED ON LASER SCANNER DATA AND TOPOGRAPHIC MAP INFORMATION , 2002 .

[9]  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.

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

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

[12]  T. Rabbani,et al.  Automatic reconstruction of industrial installations using point clouds and images , 2006 .

[13]  Daniel Cohen-Or,et al.  SmartBoxes for interactive urban reconstruction , 2010, ACM Transactions on Graphics.

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

[15]  K. Lingemann,et al.  The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design , 2011 .

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

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

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

[19]  Andrew Zisserman,et al.  Robust computation and parametrization of multiple view relations , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[20]  Cordelia Schmid,et al.  AUTOMATIC LINE MATCHING AND 3D RECONSTRUCTION OF BUILDINGS FROM MULTIPLE VIEWS , 1999 .