# The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments

arXiv: https://arxiv.org/abs/2605.12077 · HTML: https://mlsanity.com/paper/2605.12077/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 73 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning](https://mlsanity.com/paper/1812.00329/) (2018)
- [Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers](https://mlsanity.com/paper/2205.12551/) (2022)
- [DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly](https://mlsanity.com/paper/2402.19302/) (2024)
- [Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer](https://mlsanity.com/paper/2207.11971/) (2022)
- [Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning andShortest Path Optimization](https://mlsanity.com/paper/2005.12548/) (2020)
- [Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving](https://mlsanity.com/paper/2410.24010/) (2024)
- [JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks](https://mlsanity.com/paper/2101.07555/) (2021)
- [Solving Archaeological Puzzles](https://mlsanity.com/paper/1812.10553/) (2018)
- [Solving Jigsaw Puzzles with Eroded Boundaries](https://mlsanity.com/paper/1912.00755/) (2019)
- [A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles](https://mlsanity.com/paper/1711.06769/) (2017)
- [Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers](https://mlsanity.com/paper/2404.07292/) (2024)
- [Solving Jigsaw Puzzles with Linear Programming](https://mlsanity.com/paper/1511.04472/) (2015)
- [Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts](https://mlsanity.com/paper/2008.07644/) (2020)
- [GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image](https://mlsanity.com/paper/2207.05634/) (2022)
- [A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts](https://mlsanity.com/paper/2410.14770/) (2024)
- [JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition](https://mlsanity.com/paper/1809.04137/) (2018)
- [Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving](https://mlsanity.com/paper/2303.14793/) (2023)
- [A generative flow for conditional sampling via optimal transport](https://mlsanity.com/paper/2307.04102/) (2023)
- [DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem](https://mlsanity.com/paper/1711.08762/) (2017)
- [Pairwise Alignment & Compatibility for Arbitrarily Irregular Image Fragments](https://mlsanity.com/paper/2507.09767/) (2025)
- [A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels](https://mlsanity.com/paper/1912.02707/) (2019)
- [ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps](https://mlsanity.com/paper/2504.09608/) (2025)
- [TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries](https://mlsanity.com/paper/2203.06488/) (2022)
- [VLHSA: Vision-Language Hierarchical Semantic Alignment for Jigsaw Puzzle Solving with Eroded Gaps](https://mlsanity.com/paper/2509.25202/) (2025)

## Built on by (most cited first, 3 of 3)

- [JigSync: Gauge-Resolved Synchronization for Jigsaw Reassembly under Unknown Piece Orientation](https://mlsanity.com/paper/2609.18379/) (2026)
- [An Invariant Tangent-Angle Descriptor and a Band U-Net for 2D Fragment Adjacency Prediction](https://mlsanity.com/paper/2610.09459/) (2026)
- [Fix the Match, Not the Solver: STITCH -- a Self-Supervised Compatibility Measure for Eroded Visual Puzzles](https://mlsanity.com/paper/2610.13176/) (2026)

## Abstract (arXiv)

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.