Boundary points based scale invariant 3D point feature

Abstract In this paper, we propose a method for encoding scale invariant 3D point features. We extract a set of boundary points from a point cloud. Next, we apply the scale-space concept on the boundary points to detect the scale invariant point border. We confirm three orthometric axes as the local reference frames. Three distribution matrices are generated by implementing the strategy of SPIN image method, and one-row-vector of descriptors are finally calculated. Experimental results on simulated and real scene point clouds demonstrate that the scale-invariant features of 3D point clouds can be effectively encoded by our method.

[1]  Michael J. Black,et al.  FAUST: Dataset and Evaluation for 3D Mesh Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Le Xiao,et al.  SIPF: Scale invariant point feature for 3D point clouds , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[3]  Radu Horaud,et al.  Surface feature detection and description with applications to mesh matching , 2009, CVPR.

[4]  Jan-Michael Frahm,et al.  3D model matching with Viewpoint-Invariant Patches (VIP) , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Umberto Castellani,et al.  Sparse points matching by combining 3D mesh saliency with statistical descriptors , 2008, Comput. Graph. Forum.

[6]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[8]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[9]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Henrik Gordon Petersen,et al.  In Search of Inliers: 3D Correspondence by Local and Global Voting , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Kazufumi Kaneda,et al.  Scale alignment of 3D point clouds with different scales , 2014, Machine Vision and Applications.

[12]  Vincent Lepetit,et al.  Robust 3D Tracking with Descriptor Fields , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Mohammed Bennamoun,et al.  Rotational Projection Statistics for 3D Local Surface Description and Object Recognition , 2013, International Journal of Computer Vision.

[14]  T. Lindeberg.  Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales , 1994 .

[15]  Roberto Scopigno,et al.  Relative Scale Estimation and 3D Registration of Multi-Modal Geometry Using Growing Least Squares , 2016, IEEE Transactions on Visualization and Computer Graphics.

[16]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[17]  Gary R. Bradski,et al.  Fast 3D recognition and pose using the Viewpoint Feature Histogram , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Yu Zhong,et al.  Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[19]  Kazufumi Kaneda,et al.  3D Keypoints Detection from a 3D Point Cloud for Real-Time Camera Tracking , 2013 .

[20]  Nanning Zheng,et al.  AN Extension of the ICP Algorithm Considering Scale Factor , 2007, 2007 IEEE International Conference on Image Processing.

[21]  Alexei A. Efros,et al.  Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Kazufumi Kaneda,et al.  Scale ratio ICP for 3D point clouds with different scales , 2013, 2013 IEEE International Conference on Image Processing.

[23]  Marcel Körtgen,et al.  3D Shape Matching with 3D Shape Contexts , 2003 .

[24]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[25]  Luc Van Gool,et al.  Hough Transform and 3D SURF for Robust Three Dimensional Classification , 2010, ECCV.

[26]  Nico Blodow,et al.  Persistent Point Feature Histograms for 3D Point Clouds , 2008 .

[27]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[28]  Martial Hebert,et al.  Multi-scale interest regions from unorganized point clouds , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[29]  Michael Beetz,et al.  Detecting and segmenting objects for mobile manipulation , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[30]  Andrew E. Johnson,et al.  Surface matching for object recognition in complex three-dimensional scenes , 1998, Image Vis. Comput..

[31]  Ghassan Hamarneh,et al.  n -SIFT: n -Dimensional Scale Invariant Feature Transform , 2009, IEEE Trans. Image Process..

[32]  Katsushi Ikeuchi,et al.  Robust 3D Features for Matching between Distorted Range Scans Captured by Moving Systems , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[34]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[35]  Benjamin Bustos,et al.  Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes , 2011, The Visual Computer.

[36]  Meng Wang,et al.  3D deep shape descriptor , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[38]  R. Cattell,et al.  The Procrustes Program: Producing direct rotation to test a hypothesized factor structure. , 2007 .

[39]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

[40]  Andrea Torsello,et al.  A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes , 2013, International Journal of Computer Vision.

[41]  Markus Vincze,et al.  Ensemble of shape functions for 3D object classification , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[42]  Jianxiong Xiao,et al.  Semantic alignment of LiDAR data at city scale , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[44]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Yosi Keller,et al.  Scale-Invariant Features for 3-D Mesh Models , 2012, IEEE Transactions on Image Processing.