Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles

• URL: http://arxiv.org/abs/2507.07828v1
• Date: Thu, 10 Jul 2025 15:01:23 GMT
• Title: Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles
• Abstract summary: We evaluate content-based puzzle solvers for three types of jigsaw puzzle corruptions: missing pieces, eroded edges, and eroded contents.<n>Deep learning models can significantly improve their robustness through fine-tuning with augmented data.<n>We highlight promising research directions for enhancing the automated reconstruction of real-world artefacts.
• Score: 2.06242362470764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Content-based puzzle solvers have been extensively studied, demonstrating significant progress in computational techniques. However, their evaluation often lacks realistic challenges crucial for real-world applications, such as the reassembly of fragmented artefacts or shredded documents. In this work, we investigate the robustness of State-Of-The-Art content-based puzzle solvers introducing three types of jigsaw puzzle corruptions: missing pieces, eroded edges, and eroded contents. Evaluating both heuristic and deep learning-based solvers, we analyse their ability to handle these corruptions and identify key limitations. Our results show that solvers developed for standard puzzles have a rapid decline in performance if more pieces are corrupted. However, deep learning models can significantly improve their robustness through fine-tuning with augmented data. Notably, the advanced Positional Diffusion model adapts particularly well, outperforming its competitors in most experiments. Based on our findings, we highlight promising research directions for enhancing the automated reconstruction of real-world artefacts.

Related papers

• The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments [3.0427549266235125]
  We introduce GAP, a set of novel jigsaw puzzles datasets containing synthetic, heavily eroded pieces of unrestricted shapes.<n>We also introduce PuzzleFlow, a novel ViT and Flow-Matching based framework for jigsaw puzzle solving, capable of handling complex puzzle pieces.
  arXiv  Detail & Related papers  (2026-05-12T13:04:28Z)
• Solving Jigsaw Puzzles in the Wild: Human-Guided Reconstruction of Cultural Heritage Fragments [4.9193756988932416]
  We propose a human-in-the-loop (HIL) puzzle solving framework to address the complexity and scale of real-world cultural heritage reconstruction.<n>Our approach integrates an automatic relaxation-labeling solver with interactive human guidance, allowing users to iteratively lock verified placements, correct errors, and guide the system toward semantically and geometrically coherent assemblies.
  arXiv  Detail & Related papers  (2026-03-06T15:37:10Z)
• Solving Convex Partition Visual Jigsaw Puzzles [3.0427549266235125]
  Jigsaw puzzle solving requires rearrangement of unordered pieces into their original pose in order to reconstruct a coherent whole.<n>Most of the literature has focused on developing solvers for square jigsaw puzzles, severely limiting their practical use.<n>In this work, we significantly expand the types of puzzles handled computationally, focusing on what is known as Convex Partitions.
  arXiv  Detail & Related papers  (2025-11-06T15:22:46Z)
• Executable Knowledge Graphs for Replicating AI Research [65.41207324831583]
  Executable Knowledge Graphs (xKG) is a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature.<n>Code will released at https://github.com/zjunlp/xKG.
  arXiv  Detail & Related papers  (2025-10-20T17:53:23Z)
• PuzzleWorld: A Benchmark for Multimodal, Open-Ended Reasoning in Puzzlehunts [47.92619068073141]
  We introduce PuzzleWorld, a large-scale benchmark of 667 puzzlehunt-style problems designed to assess step-by-step, open-ended, and creative multimodal reasoning.<n>Most state-of-the-art models achieve only 1-2% final answer accuracy, with the best model solving only 14% of puzzles and reaching 40% stepwise accuracy.<n>Our error analysis reveals that current models exhibit myopic reasoning, are bottlenecked by the limitations of language-based inference, and lack sketching capabilities crucial for visual and spatial reasoning.
  arXiv  Detail & Related papers  (2025-06-06T16:17:09Z)
• PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving [50.50405233978406]
  We propose a fully dynamic multimodal evaluation framework, named Open-ended Visual Puzzle Generation (OVPG)<n>OVPG aims to generate fresh, diverse, and verifiable evaluation data automatically in puzzle-solving tasks.<n>Built upon OVPG, we construct PuzzleBench, a dynamic and scalable benchmark comprising 11,840 VQA samples.
  arXiv  Detail & Related papers  (2025-04-15T05:29:31Z)
• ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps [28.009783235854584]
  We propose a framework of Evolutionary Reinforcement Learning with Multi-head Puzzle Perception.<n>The proposed ERL-MPP is evaluated on the JPLEG-5 dataset with large gaps and the MIT dataset with large-scale puzzles.<n>It significantly outperforms all state-of-the-art models on both datasets.
  arXiv  Detail & Related papers  (2025-04-13T14:56:41Z)
• VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models [31.645103181716678]
  Large Vision-Language Models (LVLMs) struggle with puzzles, which require precise perception, rule comprehension, and logical reasoning.<n>We introduce VGRP-Bench, a Visual Grid Reasoning Puzzle Benchmark featuring 20 diverse puzzles.<n>Our results reveal that even the state-of-the-art LVLMs struggle with these puzzles, highlighting fundamental limitations in their puzzle-solving capabilities.
  arXiv  Detail & Related papers  (2025-03-29T12:50:38Z)
• Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving [46.073519734119266]
  Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fractures are realistic, caused by a collapse of a fresco during a World War II bombing at the Pompeii archaeological park. The dataset is multi-modal providing high resolution images with characteristic pictorial elements.
  arXiv  Detail & Related papers  (2024-10-31T15:10:38Z)
• Accelerating exploration and representation learning with offline pre-training [52.6912479800592]
  We show that exploration and representation learning can be improved by separately learning two different models from a single offline dataset. We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward can significantly improve the sample efficiency on the challenging NetHack benchmark.
  arXiv  Detail & Related papers  (2023-03-31T18:03:30Z)
• Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles [67.39567701983357]
  Video Anomaly Detection (VAD) is an important topic in computer vision. Motivated by the recent advances in self-supervised learning, this paper addresses VAD by solving an intuitive yet challenging pretext task. Our method outperforms state-of-the-art counterparts on three public benchmarks.
  arXiv  Detail & Related papers  (2022-07-20T19:49:32Z)
• Non-Rigid Puzzles [50.213265511586535]
  We present a non-rigid multi-part shape matching algorithm. We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation. Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-11-26T00:32:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.