3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor

Abstract A 3D reconstruction method using feature points is presented and the parameters used to improve the reconstruction are discussed. The precision of the 3D reconstruction is improved by combining point clouds obtained from different viewpoints using structured light. A well-known algorithm for point cloud registration is the ICP (Iterative Closest Point) that determines the rotation and translation that, when applied to one of the point clouds, places both point clouds optimally. The ICP algorithm iteratively executes two main steps: point correspondence determination and registration algorithm. The point correspondence determination is a module that, if not properly executed, can make the ICP converge to a local minimum. To overcome this drawback, two techniques were used. A meaningful set of 3D points using a technique known as SIFT (Scale-invariant feature transform) was obtained and an ICP that uses statistics to generate a dynamic distance and color threshold to the distance allowed between closest points was implemented. The reconstruction precision improvement was implemented using meaningful point clouds and the ICP to increase the number of points in the 3D space. The surface reconstruction is performed using marching cubes and filters to remove the noise and to smooth the surface. The factors that influence the 3D reconstruction precision are here discussed and analyzed. A detailed discussion of the number of frames used by the ICP and the ICP parameters is presented.

[1]  Takeshi Masuda,et al.  Registration and Integration of Multiple Range Images by Matching Signed Distance Fields for Object Shape Modeling , 2002, Comput. Vis. Image Underst..

[2]  Miguel Arias-Estrada,et al.  Iterative Closest SIFT Formulation for Robust Feature Matching , 2006, ISVC.

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

[4]  Akihiro Sugimoto,et al.  A Flexible Scene Representation for 3D Reconstruction Using an RGB-D Camera , 2013, 2013 IEEE International Conference on Computer Vision.

[5]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[6]  Mohan S. Kankanhalli,et al.  Adaptive marching cubes , 1995, The Visual Computer.

[7]  Martial Hebert,et al.  Fully automatic registration of multiple 3D data sets , 2003, Image Vis. Comput..

[8]  A. C. Neves,et al.  Automatic Epipolar Geometry Recovery Using Two Images , 2011 .

[9]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[10]  Minoru Ito.  Robot vision modelling-camera modelling and camera calibration , 1990, Adv. Robotics.

[11]  Andrea Torsello,et al.  Multiview registration via graph diffusion of dual quaternions , 2011, CVPR 2011.

[12]  Seon-Min Rhee,et al.  Two-pass ICP with Color constraint for noisy RGB-D point cloud registration , 2014, 2014 IEEE International Conference on Consumer Electronics (ICCE).

[13]  Epipolar Geometry Estimation, Metric Reconstruction and Error Analysis from Two Images , 2012 .

[14]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[15]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[16]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[17]  Mark Moll,et al.  Dynamic shape reconstruction using tactile sensors , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[18]  Thomas S. Huang,et al.  The Effect of Median Filtering on Edge Estimation and Detection , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Seiichiro Kagei,et al.  3D Reconstruction Using Low Precision Scanner , 2013 .

[20]  Richard A. Volz,et al.  Estimating 3-D location parameters using dual number quaternions , 1991, CVGIP Image Underst..

[21]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[23]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[24]  S. Druon,et al.  Color Constrained ICP for Registration of Large Unstructured 3D Color Data Sets , 2006, 2006 IEEE International Conference on Information Acquisition.

[25]  Kurt Konolige,et al.  Projected texture stereo , 2010, 2010 IEEE International Conference on Robotics and Automation.

[26]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[27]  Rogério Schmidt Feris,et al.  Fine registration of 3D point clouds with iterative closest point using an RGB-D camera , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).