Fast 2.5D Mesh Segmentation to Approximately Convex Surfaces

A range image segmentation approach is presented. A range image obtained by a 3D sensor is transformed to a 2.5D triangle mesh. This mesh is then segmented into approximately convex surfaces by an iterative procedure based on the incremental convex hull algorithm and region growing. The proposed approach is designed as a fast mid-level tool whose task is to provide suitable geometric primitives for a high-level object recognition or robot localization algorithm. The presented approach is experimentally evaluated using 3D data obtained by a Kinect sensor.

[1]  Yubao Chen,et al.  Simplified Gaussian and mean curvatures to range image segmentation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[2]  Miguel Ángel García,et al.  Coarse-to-fine approximation of range images with bounded error adaptive triangular meshes , 2007, J. Electronic Imaging.

[3]  Rae-Hong Park,et al.  Robust Adaptive Segmentation of Range Images , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Ramakant Nevatia,et al.  Recognition and localization of generic objects for indoor navigation using functionality , 1998, Image Vis. Comput..

[5]  Anil K. Jain,et al.  Segmentation and Classification of Range Images , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ioannis Pratikakis,et al.  3D Mesh Segmentation Methodologies for CAD applications , 2007 .

[7]  Xiaoyi Jiang.  Qualitative Decomposition of Range Images into Convex Parts / Objects , 2000, MVA.

[8]  S. Coleman,et al.  Single-step Planar Surface Extraction from Range Images , 2008 .

[9]  Xin Chen,et al.  Fast segmentation of range images into planar regions , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Carlos H. Caldas,et al.  Human-Assisted Obstacle Avoidance System using 3D Workspace Modeling for Construction Equipment Operation , 2006 .

[11]  Marshall W. Bern,et al.  A new Voronoi-based surface reconstruction algorithm , 1998, SIGGRAPH.

[12]  Adam Krzyzak,et al.  Robust Estimation for Range Image Segmentation and Reconstruction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  David Suter,et al.  Robust adaptive-scale parametric model estimation for computer vision , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ratko Grbic,et al.  Detection of Planar Surfaces Based on RANSAC and LAD Plane Fitting , 2009, ECMR.

[15]  Soon-Wook Kwon,et al.  Rapid Human-Assisted Creation of Bounding Models for Obstacle Avoidance in Construction , 2002 .

[16]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

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

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

[19]  Hyojoo Son,et al.  Rapid 3D object detection and modeling using range data from 3D range imaging camera for heavy equipment operation , 2010 .

[20]  James J. Little,et al.  Vision-based global localization and mapping for mobile robots , 2005, IEEE Transactions on Robotics.

[21]  Suchendra M. Bhandarkar,et al.  Integrating edge and surface information for range image segmentation , 1992, Pattern Recognit..

[22]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Jake K. Aggarwal,et al.  Significant line segments for an indoor mobile robot , 1993, IEEE Trans. Robotics Autom..