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Issue No.06 - November/December (2003 vol.23)
pp: 32-41
Peter K. Allen , Columbia University
Alejandro Troccoli , Columbia University
Benjamin Smith , Columbia University
Stephen Murray , Columbia University
Ioannis Stamos , The City University of New York
Marius Leordeanu , The City University of New York
<p>Preserving cultural heritage and historic sites is an important problem. These sites are subject to erosion and vandalism, and as long-lived artifacts, they have gone through many phases of construction, damage, and repair. Keeping accurate record of these sites using 3D model building technology lets preservationists track changes and foresee structural problems; it also allows a wider audience to virtually see and tour these sites. Due to the complexity of these sites, building 3D models is time consuming and difficult, usually involving much manual effort. This article discusses automatic methods that can reduce the time it takes to build a model. The methods use range image segmentation and feature extraction algorithms. The algorithm automatically computes pairwise registrations between individual scans, builds a topological graph, and places the scans in the same frame of reference. The methods can be extended to automate the texture mapping process as well, to create both geometric and photometric realistic models. </p>
Peter K. Allen, Alejandro Troccoli, Benjamin Smith, Stephen Murray, Ioannis Stamos, Marius Leordeanu, "New Methods for Digital Modeling of Historic Sites", IEEE Computer Graphics and Applications, vol.23, no. 6, pp. 32-41, November/December 2003, doi:10.1109/MCG.2003.1242380
1. F. Bernardini et al., "Building a Digital Model of Michelangelo'sFlorentine Pietà" IEEE Computer Graphics and Applications, Jan./Feb. 2002, pp. 59-67.
2. M. Levoy et al., "The Digital Michelangelo Project: 3D Scanning of Large Statues," Proc. Siggraph 2000, ACM Press, New York, 2000.
3. P.E. Debevec, C.J. Taylor, and J. Malik, “Modeling and Rendering Architecture from Photographs: A Hybrid Geometry- and Image-Based Approach,” Proc. SIGGRAPH '96, pp. 11-20, Aug. 1996.
4. K. Ikeuchi and Y. Sato, Modeling from Reality. Kluwer Academic Publishers, 2001.
5. K. Nuyts et al., "Vision on Conservation," Proc. Int'l Symp. Virtual and Augmented Architecture (VAA 01), B. Fischer, K. Dawson-Howe, and C. O'Sullivan, eds., Springer, 2001, pp. 125-132.
6. J. Gregor and R. Whitaker, "Indoor Scene Reconstruction from Sets of Noisy Images," Graphical Models, vol. 63, no. 5, 2002, pp. 304-332.
7. I. Stamos and M. Leordeanu, "Automated Feature-Based Range Registration of Urban Scenes of Large Scale," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. II, IEEE CS Press 2003, pp. 555-561.
8. P.J. Besl and N.D. McKay, "A Method for Registration of 3D Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
9. I. Stamos and P.K. Allen, "Geometry and Texture Recovery of Scenes of Large Scale," Computer Vision and Image Understanding (CVIU), vol. 88, no. 2, Nov. 2002, pp. 94-118.
10. O.D. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint.Cambridge, Mass.: MIT Press, 1993.
11. K. Nishino and K. Ikeuchi, "Robust Simultaneous Registration of Multiple Range Images," Proc. 5th Asian Conf. Computer Vision, 2002, pp. 454-461.
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