Solving Convex Partition Visual Jigsaw Puzzles

TL;DR

This paper introduces a new approach to solving convex partition jigsaw puzzles, expanding beyond traditional square puzzles by handling convex polygonal pieces using geometrical and pictorial compatibilities, and provides a benchmark dataset.

Contribution

It presents the first solver for convex polygonal puzzles, combining geometrical and pictorial methods, and establishes a new benchmark dataset for this puzzle type.

Findings

Introduced a greedy solver for convex partition puzzles.

Established the first benchmark dataset for convex polygonal puzzles.

Demonstrated effective performance on the new dataset.

Abstract

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.