Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles

Dirauf R, Wolz F, Zanca D, Eskofier B (2026)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer

Series: Lecture Notes in Computer Science

City/Town: Cham

Book Volume: 16167

Pages Range: 286-298

Conference Proceedings Title: Image Analysis and Processing - ICIAP 2025

Event location: Rom IT

ISBN: 978-3-032-10184-6

URI: https://link.springer.com/chapter/10.1007/978-3-032-10185-3_23

DOI: 10.1007/978-3-032-10185-3_23

Abstract

Content-based puzzle solvers have been extensively studied, demonstrating significant progress in computational techniques. However, their evaluation often lacks realistic challenges crucial for real-world applications, such as the reassembly of fragmented artefacts or shredded documents. In this work, we investigate the robustness of State-Of-The-Art content-based puzzle solvers introducing three types of jigsaw puzzle corruptions: missing pieces, eroded edges, and eroded contents. Evaluating both heuristic and deep learning-based solvers, we analyse their ability to handle these corruptions and identify key limitations. Our results show that solvers developed for standard puzzles have a rapid decline in performance if more pieces are corrupted. However, deep learning models can significantly improve their robustness through fine-tuning with augmented data. Notably, the advanced Positional Diffusion model adapts particularly well, outperforming its competitors in most experiments. Based on our findings, we highlight promising research directions for enhancing the automated reconstruction of real-world artefacts.

Authors with CRIS profile

Richard Dirauf Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Related research project(s)

Machine Learning for CT-Detector Production April 1, 2022 - July 31, 2025

Involved external institutions

Siemens Healthineers DE Germany (DE)

How to cite

APA:

Dirauf, R., Wolz, F., Zanca, D., & Eskofier, B. (2026). Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles. In Image Analysis and Processing - ICIAP 2025 (pp. 286-298). Rom, IT: Cham: Springer.

MLA:

Dirauf, Richard, et al. "Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles." Proceedings of the International Conference on Image Analysis and Processing (ICIAP), Rom Cham: Springer, 2026. 286-298.

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