缺失的 GAP：从求解方形拼图到处理真实世界考古碎片
The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments
GAP 提供了由真实考古碎片分布驱动的、形状不受限且严重侵蚀的合成拼图数据集，可用于评测形状不规则碎片的装配算法；PuzzleFlow 用 ViT 加 Flow-Matching 建模碎片间的连续匹配关系，为处理非方形、带侵蚀的碎片装配提供了可复用的建模范式。
原文依据 we introduce GAP, a set of novel jigsaw puzzles datasets containing synthetic, heavily eroded pieces of unrestricted shapes
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论拼图求解已成为计算机视觉研究界日益流行的任务。近期工作利用前沿架构与计算方法将成组碎片重新拼合成连贯图像，并在成熟数据集上取得越来越好的结果。然而，这些方法大多共享一个共同的限制性设定：仅处理严格方形的拼图碎片。在本工作中，我们提出 GAP，一组全新的拼图数据集，其中包含由真实考古碎片的学得分布生成的、形状不受限且严重侵蚀的合成碎片。我们还提出 PuzzleFlow，一个基于 ViT 与流匹配（Flow-Matching）的全新拼图求解框架，能够处理复杂拼图碎片，并在 GAP 上相比该领域经典方法与近期代表性工作展现出更优性能。
英文原文摘要 912 字符
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.