“A system for high-volume acquisition and matching of fresco fragments: reassembling Theran wall paintings” by Brown, Toler-Franklin, Nehab, Burns, Dobkin, et al. …

Although mature technologies exist for acquiring images, geometry, and normals of small objects, they remain cumbersome and time-consuming for non-experts to employ on a large scale. In an archaeological setting, a practical acquisition system for routine use on every artifact and fragment would open new possibilities for archiving, analysis, and dissemination. We present an inexpensive system for acquiring all three types of information, and associated metadata, for small objects such as fragments of wall paintings. The acquisition system requires minimal supervision, so that a single, non-expert user can scan at least 10 fragments per hour. To achieve this performance, we introduce new algorithms to robustly and automatically align range scans, register 2-D scans to 3-D geometry, and compute normals from 2-D scans. As an illustrative application, we present a novel 3-D matching algorithm that efficiently searches for matching fragments using the scanned geometry.

1. Arun, K. S., Huang, T. S., and Blostein, S. D. 1987. Least-Squares Fitting of Two 3-D Point Sets. IEEE Trans. PAMI, Vol. 9, No. 5, 698–700. Google ScholarDigital Library
2. Bernardini, F., Rushmeier, H., Martin, I. M., Mittleman, J., and Taubin, G. 2002. Building a Digital Model of Michelangelo’s Florentine Pietà. IEEE Computer Graphics and Applications, Vol. 22, No. 1, 59–67. Google ScholarDigital Library
3. Besl, P. J., and McKay, N. D. 1992. A Method for Registration of 3-D Shapes. IEEE Trans. PAMI, Vol. 14, No. 2, 239–256. Google ScholarDigital Library
4. Bracci, S., Falletti, F., Matteini, M., and Scopigno, R., Eds. 2004. Exploring David: Diagnostic Tests and State of Conservation. Giunti Press, Florence, Italy.Google Scholar
5. Brown, B. J. 2008. Registration and Matching of Large Geometric Datasets for Cultural Heritage Applications. Ph.D. thesis, Princeton Univ. Google ScholarDigital Library
6. Chen, Y., and Medioni, G. 1992. Object Modeling by Registration of Multiple Range Images. Image and Vision Computing, Vol. 10, No. 3, 145–155. Google ScholarDigital Library
7. Doumas, C. 1992. The Wall-Paintings of Thera. Thera Foundation – P. M. Nomikos, Athens.Google Scholar
8. Fornasier, M., and Toniolo, D. 2005. Fast, Robust and Efficient 2D Pattern Recognition for Re-Assembling Fragmented Images. Pattern Recognition, Vol. 38, No. 11, 2074–2087. Google ScholarDigital Library
9. Gardner, A., Tchou, C., Hawkins, T., and Debevec, P. 2003. Linear Light Source Reflectometry. ACM Trans. Graphics (Proc. SIGGRAPH), Vol. 22, No. 3, 749–758. Google ScholarDigital Library
10. Gelfand, N., Ikemoto, L., Rusinkiewicz, S., and Levoy, M. 2003. Geometrically Stable Sampling for the ICP Algorithm. In Proc. 3DIM, 260–267.Google Scholar
11. Huang, Q.-X., Flöry, S., Gelfand, N., Hofer, M., and Pottmann, H. 2006. Reassembling Fractured Objects by Geometric Matching. ACM Trans. Graphics (Proc. SIGGRAPH), Vol. 25, No. 3, 569–578. Google ScholarDigital Library
12. Johnson, A., and Hebert, M. 1997. Surface Registration by Matching Oriented Points. In Proc. 3DIM, 121–128. Google ScholarDigital Library
13. Karasik, A., and Smilansky, U. 2008. 3D Scanning Technology as a Standard Archaeological Tool for Pottery Analysis: Practice and Theory. Journal of Archaeological Science, Vol. 35, 1148–1168.Google ScholarCross Ref
14. Koller, D., Trimble, J., Najbjerg, T., Gelfand, N., and Levoy, M. 2006. Fragments of the City: Stanford’s Digital Forma Urbis Romae Project. In Proc. Third Williams Symposium on Classical Architecture, Journal of Roman Archaeology, vol. Suppl. 61, 237–252.Google Scholar
15. Kong, W., and Kimia, B. 2001. On Solving 2D and 3D Puzzles under Curve Matching. In Proc. CVPR, vol. 2, 583–590.Google Scholar
16. Leitão, H. C. G., and Stolfi, J. 2002. A Multiscale Method for the Reassembly of Two-Dimensional Fragmented Objects. IEEE Trans. PAMI, Vol. 24, No. 9, 1239–1251. Google ScholarDigital Library
17. Lensch, H. P., Heidrich, W., and Seidel, H. 2000. Automated Texture Registration and Stitching for Real World Models. In Proc. Pacific Graphics, 317–326. Google ScholarDigital Library
18. Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., and Fulk, D. 2000. The Digital Michelangelo Project: 3-D Scanning of Large Statues. In Proc. SIGGRAPH, 131–144. Google ScholarDigital Library
19. Lowe, D. G. 2004. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, Vol. 60, No. 2, 91–110. Google ScholarDigital Library
20. Nehab, D., Rusinkiewicz, S., Davis, J., and Ramamoorthi, R. 2005. Efficiently Combining Positions and Normals for Precise 3D Geometry. ACM Trans. Graphics (Proc. SIGGRAPH), Vol. 24, No. 3, 536–543. Google ScholarDigital Library
21. Papaodysseus, C., Panagopoulos, T., Exarhos, M., Triantafillou, C., Fragoulis, D., and Doumas, C. 2002. Contour-Shape Based Reconstruction of Fragmented, 1600 BC Wallpaintings. IEEE Trans. on Signal Processing, Vol. 50, No. 6, 1277–1288. Google ScholarDigital Library
22. Rusinkiewicz, S., and Levoy, M. 2001. Efficient Variants of the ICP Algorithm. In Proc. 3DIM, 145–152.Google Scholar
23. Saǧiroǧlu, M. Ş., and Erçil, A. 2006. A Texture Based Matching Approach for Automated Assembly of Puzzles. In Proc. ICPR, vol. 3, 1036–1041. Google ScholarDigital Library
24. Toler-Franklin, C., Finkelstein, A., and Rusinkiewicz, S. 2007. Illustration of Complex Real-World Objects using Images with Normals. In Proc. NPAR, 111–119. Google ScholarDigital Library
25. Vlachopoulos, A. 2008. The Wall Paintings from the Xeste 3 building at Akrotiri. Towards an Interpretation of the Iconographic Programme. In Horizons: A colloquium on the prehistory of the Cyclades, Cambridge, N. Brodie, J. Doole, G. Gavalas, and C. Renfrew, Eds., 451–465.Google Scholar
26. Wasserman, J., Camiz, F. T., Verdon, T., and Rockwell, P. 2002. Michelangelo’s Florence Pietà. Princeton Univ. Press.Google Scholar
27. Willis, A. 2004. Stochastic 3D Geometric Models for Classification, Deformation, and Estimation. Ph. D. thesis, Brown Univ. Google ScholarDigital Library
28. Woodham, R. J. 1980. Photometric Method for Determining Surface Orientation from Multiple Images. Optical Engineering, Vol. 19, No. 1, 139–144.Google ScholarCross Ref