Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles
Richard Dirauf, Florian Wolz, Dario Zanca, Bj\"orn Eskofier

TL;DR
This paper evaluates the robustness of content-based puzzle solvers against realistic corruptions like missing pieces and erosion, highlighting the strengths of deep learning models and proposing future research directions.
Contribution
It introduces a comprehensive benchmarking of puzzle solvers under realistic corruptions and demonstrates the effectiveness of fine-tuning deep learning models, especially the Positional Diffusion model.
Findings
Deep learning models outperform heuristic methods under corruptions.
Performance declines rapidly with increased corrupted pieces.
Fine-tuning with augmented data improves robustness significantly.
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…
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