Solving Jigsaw Puzzles with Eroded Boundaries

TL;DR

This paper introduces a novel method for solving jigsaw puzzles with eroded boundaries by using a GAN-based inpainting approach to improve boundary information and classify piece adjacency, outperforming existing methods.

Contribution

It presents a new approach that combines inpainting and classification within a single GAN discriminator to tackle eroded boundary puzzles, a previously challenging problem.

Findings

Outperforms state-of-the-art methods on eroded boundary puzzles

Effective boundary inpainting improves piece adjacency classification

Unified GAN discriminator simplifies training process

Abstract

Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem - solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as 'neighbors or not'. An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; Training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methods