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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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1 princeton university 3 microsoft research 5 national university of athens

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


Ribbon matching results

Ribbon 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


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