A robust pipeline for rapid feature-based pre-alignment of dense range scans

Aiming at reaching an interactive and simplified usage of high-resolution 3D acquisition systems, this paper presents a fast and automated technique for pre-alignment of dense range images. Starting from a multi-scale feature point extraction and description, a processing chain composed by feature matching and correspondence searching, ranking grouping and skimming is performed to select the most reliable correspondences over which the correct alignment is estimated. Pre-alignment is obtained in few seconds per million point images on a off-the-shelf PC architecture. The experimental setup aimed to demonstrate the system behavior with respect to a set of concurrent requirements and the obtained performance are significant in the perspective of a fast, robust and unconstrained 3D object reconstruction.

[1]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[2]  Leonidas J. Guibas,et al.  Robust global registration , 2005, SGP '05.

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

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

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

[6]  John B. Moore,et al.  Optimisation-on-a-manifold for global registration of multiple 3D point sets , 2007, Int. J. Intell. Syst. Technol. Appl..

[7]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[8]  Igor Guskov,et al.  Multi-scale features for approximate alignment of point-based surfaces , 2005, SGP '05.

[9]  Jean-Philippe Pons,et al.  Robust and Efficient Surface Reconstruction From Range Data , 2009, Comput. Graph. Forum.

[10]  David W. Jacobs,et al.  Mesh saliency , 2005, SIGGRAPH 2005.

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[12]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[13]  Alberto Signoroni,et al.  A multiscale feature extraction approach for 3D range images , 2010, 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10.

[14]  N. Mitra,et al.  4-points congruent sets for robust pairwise surface registration , 2008, SIGGRAPH 2008.

[15]  Alberto Signoroni,et al.  An Enhanced 'Optimization-on-a-Manifold' Framework for Global Registration of 3D Range Data , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[16]  Daniel Cohen-Or,et al.  4-points congruent sets for robust pairwise surface registration , 2008, ACM Trans. Graph..

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

[18]  Holly E. Rushmeier,et al.  The 3D Model Acquisition Pipeline , 2002, Comput. Graph. Forum.

[19]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[20]  Ray Jarvis,et al.  3D free-form surface registration and object recognition , 2004, International Journal of Computer Vision.

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