在受腐蚀的图片拼图上基准测试基于内容的谜题求解器
Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles
AI总结：
本文评估了三种类型的图片拼图腐蚀对基于内容的谜题求解器的影响，发现深度学习模型通过微调增强数据能显著提高鲁棒性，位置扩散模型表现尤为突出。
摘要：
基于内容的谜题求解器已被广泛研究，展示了在计算技术上的显著进展。然而，它们的评估往往缺乏对现实世界应用至关重要的真实挑战，例如碎片化文物或撕碎文件的重新组装。在本工作中，我们研究了最先进的基于内容的谜题求解器的鲁棒性，引入了三种类型的图片拼图腐蚀：缺失的拼图块、边缘侵蚀和内容侵蚀。评估了启发式和基于深度学习的求解器，分析它们处理这些腐蚀的能力，并识别关键限制。我们的结果表明，为标准谜题开发的求解器在更多拼图块被腐蚀时性能会迅速下降。然而，深度学习模型可以通过使用增强数据进行微调显著提高其鲁棒性。值得注意的是，先进的位置扩散模型适应性特别好，在大多数实验中优于其竞争对手。基于我们的发现，我们强调了增强现实世界文物自动重建的有前途的研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
