PuzzleSorter: Certainty-Aware Visual Restoration of Multiple Cultural Artifacts

We present PuzzleSorter, a certainty-aware visual analytics system for cultural relic fragment restoration. Restoring cultural objects from broken fragments is a fundamental task in geometry and archaeology. Prior research proposes automatic models to classify fragments by types and assemble matched pairs successively. However, eroded fragments lead to erroneous results, posing two challenges for restorers to correct: (1) numerous fragments conceal errors within an overwhelming number of object appearances, and (2) the unknown difficulty of restoration hinders correction strategy development. To address these challenges, PuzzleSorter provides multi-criteria analysis that helps users identify certainties of current solutions and alternatives at the type, object, and fragment levels. Moreover, our system visualizes these certainties through a relation graph, which implies alternative assembly solutions with geometric context and indicates correction difficulties through neighbor proximity, number of neighbors, and path length. We demonstrate the feasibility and utility of our system through two case studies and expert interviews.

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Publication Details

Journal
Computational Visual Media
Published
2025-06-30
DOI
https://doi.org/10.26599/cvm.2025.9450468
Citations
1
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
1.28
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article

PuzzleSorter: Certainty-Aware Visual Restoration of Multiple Cultural Artifacts

Changyu Diao, Tan Tang, Rongqin Yu, Shuainan Ye et al.
1 citations
Computational Visual Media
Image Processing and 3D Reconstruction
1.28
article

PuzzleSorter: Certainty-Aware Visual Restoration of Multiple Cultural Artifacts

Changyu Diao, Tan Tang, Rongqin Yu, Shuainan Ye, Lu Jiang, Lingyun Yu, Yingcai Wu, Jianing Yin, Buwei Zhou
article en
1 citations

Abstract

We present PuzzleSorter, a certainty-aware visual analytics system for cultural relic fragment restoration. Restoring cultural objects from broken fragments is a fundamental task in geometry and archaeology. Prior research proposes automatic models to classify fragments by types and assemble matched pairs successively. However, eroded fragments lead to erroneous results, posing two challenges for restorers to correct: (1) numerous fragments conceal errors within an overwhelming number of object appearances, and (2) the unknown difficulty of restoration hinders correction strategy development. To address these challenges, PuzzleSorter provides multi-criteria analysis that helps users identify certainties of current solutions and alternatives at the type, object, and fragment levels. Moreover, our system visualizes these certainties through a relation graph, which implies alternative assembly solutions with geometric context and indicates correction difficulties through neighbor proximity, number of neighbors, and path length. We demonstrate the feasibility and utility of our system through two case studies and expert interviews.

Computational Visual MediaVol. 11(6)
Institute of Archaeology (CN), Xi’an Jiaotong-Liverpool University (CN), Zhejiang University (CN)
Sustainable cities and communities
Openalex Percentile: Top 19%
Image Processing and 3D Reconstruction
1.28
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PuzzleSorter: Certainty-Aware Visual Restoration of Multiple Cultural Artifacts — Changyu Diao, Tan Tang, et al. · Computational Visual Media (2025) | TGRS Research Map | TGRS