# JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

arXiv: https://arxiv.org/abs/1809.04137 · HTML: https://mlsanity.com/paper/1809.04137/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving](https://mlsanity.com/paper/2410.24010/) (2024)
- [PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving](https://mlsanity.com/paper/2211.13785/) (2022)
- [The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments](https://mlsanity.com/paper/2605.12077/) (2026)
- [ReassembleNet: Learnable Keypoints and Diffusion for 2D Fresco Reconstruction](https://mlsanity.com/paper/2505.21117/) (2025)
- [Solving Convex Partition Visual Jigsaw Puzzles](https://mlsanity.com/paper/2511.04450/) (2025)
- [Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition](https://mlsanity.com/paper/2604.17062/) (2026)
- [An Invariant Tangent-Angle Descriptor and a Band U-Net for 2D Fragment Adjacency Prediction](https://mlsanity.com/paper/2610.09459/) (2026)

## Abstract (arXiv)

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces.