A novel method based on color information for scanned data alignment
Authors: 
Shen Yang, Yue Qi, Fei Hou, Xukun Shen, Qinping ZhaoAuthors Info & Claims
Shen Yang, Yue Qi, Fei Hou, Xukun Shen, Qinping ZhaoAuthors Info & ClaimsPages 201 - 204
Abstract
This paper presents a rapid and robust method to align large sets of range scans captured by a 3D scanner automatically. The method incorporates the color information from the range data into the pairwise registration. Firstly, it detects the features using SIFT (Scale-Invariant Feature Transform) on grayscale images generated from two range scans to align. Then a quasi-dense matching algorithm, based on the match propagation principle, is applied to specify the matching pixel pairs between two images. All matches obtained are mapped to 3D space but in different world coordinates, and fitered by the 3D geometry constraint discovered from the range data. The remaining set of point correspondences is used to estimate the rigid transformation. Finally, a modified ICP (Iterative Closest Point) algorithm is applied to refine the result. The paper also describes a framework to use this alignment method for object reconstruction. The reconstruction proceeds by acquiring several range scans with color information from different directions, following which pair-wise of range data are aligned with the above method selectively and iteratively. Then a model graph containing the correct pair-wise matches is created and a span tree specifying a complete model is constructed. Finally a global optimization is performed to refine the result. This reconstruction technique achieves a robust and high performance in the application of rebuilding the 3D models of culture heritages for virtual museum automatically.
References
[1]
Bendels, G. H., Degener, P., Wahl, R., Körtgen, M., and Klein, R. 2004. Image-based registration of 3d-range data using feature surface elements. In VAST, 115--124.
[2]
Besl, P. J., and McKay, N. D. 1992. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 2, 239--256.
[3]
Chen, Y., and Medioni, G. 1992. Object modelling by registration of multiple range images. Image Vision Comput. 10, 3, 145--155.
[4]
Chen, C.-S., Hung, Y.-P., and Cheng, J.-B. 1999. Ransac-based darces: A new approach to fast automatic registration of partially overlapping range images. IEEE Trans. Pattern Anal. Mach. Intell. 21, 11, 1229--1234.
[5]
Gelfand, N., Mitra, N. J., Guibas, L. J., and Pottmann, H. 2005. Robust global registration. In Symposium on Geometry Processing, 197--206.
[6]
Hartley, R., and Zisserman, A. 2003. Multiple View Geometry in Computer Vision. Cambridge University Press, New York, NY, USA.
[7]
Horn, B. 1987. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 4, 629--642.
[8]
Huber, D. F. 2002. Automatic three-dimensional modeling from reality. PhD thesis, Carnegie Mellon University. Chair-Martial Hebert.
[9]
Lhuillier, M., and Quan, L. 2002. Match propogation for image-based modeling and rendering. IEEE Trans. Pattern Anal. Mach. Intell. 24, 8, 1140--1146.
[10]
Li, X., and Guskov, I. 2005. Multi-scale features for approximate alignment of point-based surfaces. In SGP '05: Proceedings of the third Eurographics symposium on Geometry processing, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 217.
[11]
Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 2, 91--110.
[12]
Pingi, P., Fasano, A., Cignoni, P., Montani, C., and Scopigno, R. 2005. Exploiting the scanning sequence for automatic registration of large sets of range maps. Computer Graphics Forum 24, 3, 517--526.
Index Terms
- A novel method based on color information for scanned data alignment
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Information & Contributors
Information
Published In
October 2008
288 pages
ISBN:9781595939517
DOI:10.1145/1450579
- Conference Chairs:
- Steven Feiner,
- Daniel Thalmann,
- Pascal Guitton,
- Program Chairs:
- Bernd Fröhlich,
- Ernst Kruijff,
- Martin Hachet
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Publication History
Published: 27 October 2008
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- Research-article
Funding Sources
- Ministry of Education of the People's Republic of China
- Beijing Municipal Science and Technology Commission
- Ministry of Science and Technology of the People's Republic of China
- National Natural Science Foundation of China
Conference
VRST08: The ACM Symposium on Virtual Reality Software and Technology
October 27 - 29, 2008
Bordeaux, France
Acceptance Rates
VRST '08 Paper Acceptance Rate 12 of 68 submissions, 18%;
Overall Acceptance Rate 66 of 254 submissions, 26%
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