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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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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

bronze age thera
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

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

Thera Foundation

akrotiri1
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
the akrotiri jigsaw
The Akrotiri Jigsaw
  • Current assembly process is laborious
the akrotiri jigsaw1
The Akrotiri Jigsaw
  • Current assembly process is laborious
  • Enough work for another century
fragment characteristics
Fragment Characteristics

Conservators consider:

size, thickness

level of erosion

discoloration and fading

set of pigments

curvature / flatness

texture of the back

string impressions

constrained 3 d acquisition protocol
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
color and normals 2 d acquisition
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
scan alignment with multi way icp
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
front back alignment
Front/Back Alignment
  • Flipping fragment is uncalibrated
  • Little overlap between front and back scans
  • Front/back alignment is vertically unstable
front back alignment1
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
2 d 3 d alignment
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

ribbon matching
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching1
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching2
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching3
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching4
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching5
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching6
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
ribbon matching7
Ribbon Matching
  • Try all possible alignments
  • Update alignment incrementally
  • Regular edge parameterization:similar to image correlation
fragment matching
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

erosion detection
Erosion Detection
  • Erosion causes incorrect alignments
  • Detected on ribbons with normal constraint

Fragment Front

No Erosion Detection

Fragment Back

erosion detection1
Erosion Detection
  • Erosion causes incorrect alignments
  • Detected on ribbons with normal constraint

Fragment Front

No Erosion Detection

With Erosion Detection

Fragment Back

outline
Outline
  • System design
  • Processing pipeline
  • Matching
  • Results
synthetic fresco
Synthetic Fresco

25 mm strip width

12.5 mm strip width

50 mm strip width

future work matching
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
future work scanners
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
future work scanners1
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
acknowledgments
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