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1 Princeton University 3 Microsoft Research 5 National University of Athens

A System for High-Volume Acquisition and Matching of Fresco Fragments Reassembling Theran Wall Paintings. Benedict Brown 1,2 , Corey Toler-Franklin 1 , Diego Nehab 1,3 , Michael Burns 1 , Andreas Vlachopoulos 4 , Christos Doumas 4,5 , David Dobkin 1 , Szymon Rusinkiewicz 1 , Tim Weyrich 1,6.

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1 Princeton University 3 Microsoft Research 5 National University of Athens

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  1. A System forHigh-Volume Acquisition and Matching of Fresco FragmentsReassembling Theran Wall Paintings Benedict Brown1,2, Corey Toler-Franklin1, Diego Nehab1,3,Michael Burns1, Andreas Vlachopoulos4, Christos Doumas4,5,David Dobkin1, Szymon Rusinkiewicz1, Tim Weyrich1,6 1Princeton University 3Microsoft Research 5National University of Athens 2Katholieke Universiteit Leuven 4Akrotiri Excavations, Thera 6University College, London

  2. Bronze Age Thera • Modern day Santorini • Aegean civilization: c. 1700 BC • Traded with other Mediterranean civilizations • Evidence of fishing, agriculture, and livestock • Volcanic eruption c. 1650 BC NASA Visible Earth

  3. Akrotiri • Major archaeological excavation since 1967 • Well-preserved by ash • Most significant find: plaster wall paintings • Pigments excellently preserved Thera Foundation

  4. Akrotiri • Major archaeological excavation since 1967 • Well-preserved by ash • Most significant find: plaster wall paintings • Pigments excellently preserved • But shattered in pieces by earthquake

  5. The Akrotiri Jigsaw • Current assembly process is laborious

  6. The Akrotiri Jigsaw • Current assembly process is laborious • Enough work for another century

  7. Fragment Characteristics Conservators consider: size, thickness level of erosion discoloration and fading set of pigments curvature / flatness texture of the back string impressions

  8. Constrained 3-D Acquisition Protocol • Automatic turntable control • Acquire scans at 45° • Two 360°scan sequences • Face-down: front face at known plane • Face-up: front face visible

  9. Color and Normals: 2-D Acquisition • Custom scan software • One-click acquisition • Preview scan locates fragment • Five scans • Four front orientations (photometric normals) • One back orientation

  10. Scan Alignment with Multi-Way ICP • Align fragments scanned on turntable • Axis of rotation gives initial guess • Standard algorithm to improve alignments:Iterative Closest Points [Besl 1992], [Chen 1992] • Flat front surfaces lead to instability • Improved algorithm: Multi-way ICP • Constrain all scan-to-scantransformations to be identical • Equivalent to solving fora single rotation axis

  11. Front/Back Alignment • Flipping fragment is uncalibrated • Little overlap between front and back scans • Front/back alignment is vertically unstable

  12. Front/Back Alignment • Use front face to determinevertical alignment • Visible in front scans • On (calibrated) turntablesurface in back scans • Initial guess and ICP forwithin-plane alignment

  13. 2-D/3-D Alignment • Flatbed scanner has superior color • Can’t use calibration [Levoy 2000], reliable silhouette [Lensch 2000], or features [Liu 2006][Chen 2007] • Use image alignment: PCA + downhill simplex Projected 3-D Color Flatbed Scan

  14. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  15. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  16. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  17. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  18. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  19. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  20. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  21. Ribbon Matching • Try all possible alignments • Update alignment incrementally • Regular edge parameterization:similar to image correlation

  22. Fragment Matching ICP Matching • Nearest neighbor correspondence search • Iterate to find matches • 45 seconds per fragment pair Ribbon Matching • Regular edge sampling for correspondences • Exhaustive search with incremental update • 2 seconds per pair Original (irregular) mesh Resampled ribbon

  23. Erosion Detection • Erosion causes incorrect alignments • Detected on ribbons with normal constraint Fragment Front No Erosion Detection Fragment Back

  24. Erosion Detection • Erosion causes incorrect alignments • Detected on ribbons with normal constraint Fragment Front No Erosion Detection With Erosion Detection Fragment Back

  25. Outline • System design • Processing pipeline • Matching • Results

  26. Ribbon Matching Results

  27. Synthetic Fresco 25 mm strip width 12.5 mm strip width 50 mm strip width

  28. Future Work (Matching) • Multi-cue matching • Improved ribbon matching/Handling gaps • Dynamic programming can probablyhandle gaps • Record all possible alignments instead of only best candidates to do saliency analysis • Global matching • Fuse matched fragments and re-match • Do global consistency checks on networks of matches

  29. Future Work (Scanners) We want to scan: • large fragments • assembled edges? • edge and back normals Approach: • Hand-held scanner • Two cameras and a projector/fixed pattern • Alignment similar to in-hand scanner • Should be able to get normals from mutiple views

  30. Future Work (Scanners) We want to scan: • large fragments • assembled edges? • edge and back normals Approach: • Hand-held scanner • Two cameras and a projector/fixed pattern • Alignment similar to in-hand scanner • Should be able to get normals from mutiple views

  31. Acknowledgments • Princeton University: Tom Funkhouser, Dimitris Gondicas,Matt Plough, Phil Shilane, Xiaojuan Ma • Akrotiri Excavation, Laboratory of Wall Paintings:Manolis Hamaoui, Litsa Kalambouki, Marina Papapetrou, Panagiotis Vlachos, Alexandros Zokos, Iakovos Michailidis, Fragoula Georma, Niki Spanou • Special thanks to David Koller (University of Viriginia),Misha Kazhdan (Johns Hopkins University), and Peter Nomikos Jr. • Funding: Thera Foundation, Kress Foundation,Seeger Foundation, Cotsen Family Foundation, andNSF Grants CCF-0347427 and CCF-0702580

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