Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts

[2008.07644] Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2008.07644 (cs) [Submitted on 17 Aug 2020 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts Authors: Peleg Harel Ofir Itzhak Shahar , Ohad Ben-Shahar View a PDF of the paper titled Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts, by Peleg Harel Ofir Itzhak Shahar and 1 other authors View PDF HTML (experimental) Abstract: Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered visual fragments, is fundamental to numerous applications, and yet most of the literature of the last two decades has focused thus far on less realistic puzzles whose pieces are identical squares. Here, we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts, a generation model inspired by the celebrated Lazy caterer sequence. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to show that they are solvable completely automatically. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Computational Geometry (cs.CG) Cite as: arXiv:2008.07644 [cs.CV] (or arXiv:2008.07644v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2008.07644 Focus to learn more arXiv-issued DOI via DataCite Journal reference: In the International Journal of Computer Vision, 132(9), pp 3428-3462, 2024 Submission history From: Ohad Ben-Shahar [ view email ] [v1] Mon, 17 Aug 2020 22:07:40 UTC (2,467 KB) [v2] Thu, 16 Dec 2021 15:32:53 UTC (17,322 KB) [v3] Tue, 14 Apr 2026 08:57:55 UTC (8,123 KB) Full-text links: Access Paper: View a PDF of the paper titled Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts, by Peleg Harel Ofir Itzhak Shahar and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2020-08 Change to browse by: cs cs.AI cs.CG References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Ohad Ben-Shahar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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