提出真实考古碎片的非规则拼图数据集与新方法，突破传统方块拼图限制。
The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments
- 基于真实考古碎片分布生成非规则形状拼图数据集
- 新框架在复杂碎片上实现更优拼合效果，超越经典与前沿方法
- 适合关注真实场景图像重建与视觉生成的科研人员
拼图复原是计算机视觉领域的热门研究任务。现有方法大多仅处理严格方形拼图块，限制了实际应用。本文提出GAP数据集，通过学习真实考古碎片的分布，生成具有高度侵蚀特征的非规则形状合成拼图块。同时引入PuzzleFlow框架，结合ViT与流匹配机制，可有效处理复杂碎片形态。实验表明，该框架在GAP数据集上的表现显著优于经典及近期主流方法，验证了其在真实世界场景下的鲁棒性与有效性。
原文摘要 · Abstract (English)
Jigsaw puzzle solving has been an increasingly popular task in the computer vision research community. Recent works have utilized cutting-edge architectures and computational approaches to reassemble groups of pieces into a coherent image, while achieving increasingly good results on well established datasets. However, most of these approaches share a common, restricting setting: operating solely on strictly square puzzle pieces. In this work, we introduce GAP, a set of novel jigsaw puzzles datasets containing synthetic, heavily eroded pieces of unrestricted shapes, generated by a learned distribution of real-world archaeological fragments. We also introduce PuzzleFlow, a novel ViT and Flow-Matching based framework for jigsaw puzzle solving, capable of handling complex puzzle pieces and demonstrating superior performance on GAP when compared to both classic and recent prominent works in this domain.
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