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.

翻译：拼图求解需要将无序碎片重新排列至原始位姿以重建连贯整体（通常为图像），该问题已被证明属于难解问题。尽管自动拼图求解器在多个应用领域可能产生颠覆性影响，现有研究大多集中于开发方形拼图求解器，严重限制了其实际应用。本研究在计算处理拼图类型方面取得显著拓展，聚焦于多边形拼图的主要子集——凸分割拼图（其碎片均为凸多边形）。我们综合运用几何相容性与图像相容性，提出一种贪婪求解算法，并在首个此类拼图基准数据集上报告了多项性能指标。