解决凸分区拼图难题，拓展了自动拼图的应用范围。
Solving Convex Partition Visual Jigsaw Puzzles
- 结合几何与图像兼容性，设计贪心求解算法。
- 首次构建凸分区拼图基准数据集，验证算法性能。
- 适用于非正方形多边形拼图，适合实际场景应用。
拼图求解需要将无序的拼图块重新排列到原始位置以重构完整图像，这一问题通常被认为是难以求解的。尽管自动拼图求解器在多个应用领域具有颠覆性潜力，但现有研究大多集中于正方形拼图，严重限制了其实际应用。本文显著扩展了可计算处理的拼图类型，聚焦于被称为凸分区的多边形拼图子集，其拼图块均为凸形。我们同时利用几何与图像兼容性，提出一种贪心求解器，并报告了多项性能指标，同时发布了首个此类拼图的基准数据集。
原文摘要 · Abstract (English)
Jigsaw puzzle solving requires the rearrangement of unordered pieces into their original pose in order to reconstruct a coherent whole, often an image, and is known to be an intractable problem. While the possible impact of automatic puzzle solvers can be disruptive in various application domains, most of the literature has focused on developing solvers for square jigsaw puzzles, severely limiting their practical use. In this work, we significantly expand the types of puzzles handled computationally, focusing on what is known as Convex Partitions, a major subset of polygonal puzzles whose pieces are convex. We utilize both geometrical and pictorial compatibilities, introduce a greedy solver, and report several performance measures next to the first benchmark dataset of such puzzles.
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