The digital Michelangelo project

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The digital Michelangelo project: 3D scanning of large statues

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International Conference on Computer Graphics and Interactive Techniques archive
Proceedings of the 27th annual conference on Computer graphics and interactive techniques table of contents

Pages: 131 - 144

Year of Publication: 2000

ISBN:1-58113-208-5

Authors

Marc Levoy	Computer Science Department, Stanford University
Kari Pulli	Computer Science Department, Stanford University
Brian Curless	Department of Computer Science and Engineering, University of Washington
Szymon Rusinkiewicz	Computer Science Department, Stanford University
David Koller	Computer Science Department, Stanford University
Lucas Pereira	Computer Science Department, Stanford University
Matt Ginzton	Computer Science Department, Stanford University
Sean Anderson	Computer Science Department, Stanford University
James Davis	Computer Science Department, Stanford University
Jeremy Ginsberg	Computer Science Department, Stanford University
Jonathan Shade	Department of Computer Science and Engineering, University of Washington
Duane Fulk	Cyberware Inc.

Sponsor

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

ACM Press/Addison-Wesley Publishing Co. New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 23, Downloads (12 Months): 108, Citation Count: 205

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ABSTRACT

We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rangefinders, laser time-of-flight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merging, and viewing scanned data. As a demonstration of this system, we digitized 10 statues by Michelangelo, including the well-known figure of David, two building interiors, and all 1,163 extant fragments of the Forma Urbis Romae, a giant marble map of ancient Rome. Our largest single dataset is of the David - 2 billion polygons and 7,000 color images. In this paper, we discuss the challenges we faced in building this system, the solutions we employed, and the lessons we learned. We focus in particular on the unusual design of our laser triangulation scanner and on the algorithms and software we developed for handling very large scanned models.

REFERENCES

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	Sylvain Paris , Will Chang , Oleg I. Kozhushnyan , Wojciech Jarosz , Wojciech Matusik , Matthias Zwicker , Frédo Durand, Hair photobooth: geometric and photometric acquisition of real hairstyles, ACM Transactions on Graphics (TOG), v.27 n.3, August 2008
	Shuntaro Yamazaki , Srinivasa G. Narasimhan , Simon Baker , Takeo Kanade, The Theory and Practice of Coplanar Shadowgram Imaging for Acquiring Visual Hulls of Intricate Objects, International Journal of Computer Vision, v.81 n.3, p.259-280, March 2009
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	Mikhail Bessmeltsev, Self-organizing approach to moving surface reconstruction, Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision, p.99-104, August 20-22, 2009, Moscow, Russia
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	Jing Wang , Kristin J. Dana, Relief Texture from Specularities, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.28 n.3, p.446-457, March 2006
	Patrick Pfaff , Rudolph Triebel , Wolfram Burgard, An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing, International Journal of Robotics Research, v.26 n.2, p.217-230, February 2007
	Yunzhen Li , Kok-Lim Low, Automatic registration of color images to 3D geometry, Computer Graphics International, p.21-28, May 26-29, 2009, Victoria, British Columbia, Canada
	Leif Kobbelt , Mario Botsch, A survey of point-based techniques in computer graphics, Computers and Graphics, v.28 n.6, p.801-814, December, 2004
	Philip L. Worthington, Reillumination-driven shape from shading, Computer Vision and Image Understanding, v.98 n.2, p.325-343, May 2005
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