A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation

[2402.12797] A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation 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:2402.12797 (cs) [Submitted on 20 Feb 2024 ( v1 ), last revised 9 Apr 2026 (this version, v4)] Title: A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation Authors: Guoqing Zhang , Yang Li View a PDF of the paper titled A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation, by Guoqing Zhang and Yang Li View PDF HTML (experimental) Abstract: We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed distance between a voxel center and the object is computed through a simple geometric algorithm. Our method does not involve any surface sampling scheme or solving large matrix equations, and therefore is a faster and more elegant solution for tubular shape reconstruction compared to other approaches. Experiments demonstrate the efficiency and effectiveness of the proposed method. Code is avaliable at this https URL . Comments: 9 pages (without reference), 6 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Computational Geometry (cs.CG) Cite as: arXiv:2402.12797 [cs.CV] (or arXiv:2402.12797v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2402.12797 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/978-981-97-5128-0_10 Focus to learn more DOI(s) linking to related resources Submission history From: Guoqing Zhang [ view email ] [v1] Tue, 20 Feb 2024 08:14:53 UTC (7,271 KB) [v2] Sat, 11 May 2024 07:17:52 UTC (7,271 KB) [v3] Mon, 1 Jul 2024 15:46:25 UTC (7,271 KB) [v4] Thu, 9 Apr 2026 08:44:05 UTC (7,271 KB) Full-text links: Access Paper: View a PDF of the paper titled A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation, by Guoqing Zhang and Yang Li View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-02 Change to browse by: cs cs.CG References & Citations NASA ADS Google Scholar Semantic Scholar 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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