C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization

[2511.00260] C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2511.00260 (cs) [Submitted on 31 Oct 2025 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization Authors: Linzhe Jiang , Jiayuan Huang , Sophia Bano , Matthew J. Clarkson , Zhehua Mao , Mobarak I. Hoque View PDF HTML (experimental) Abstract: Anatomy-aware colonoscopic navigation requires localizing partial endoscopic observations on a stable 3D reference to support coverage assessment, revisited-region awareness, and CT-guided navigation. However, rigid point cloud registration in the colon differs fundamentally from standard benchmarks: surfaces are locally homogeneous, haustral folds are repetitive, views are highly partial, and reconstructed depth is noisy. We present C3VDReg, a dataset and benchmark derived from the Colonoscopy 3D Video Dataset (C3VD). For each frame, C3VDReg generates source point clouds via depth reprojection and target point clouds by raycasting CT meshes from matched camera poses. The benchmark comprises 10,015 viewpoint-matched partial-to-partial point cloud pairs (including 2,088 held-out test pairs) and evaluates baseline models under a standardized protocol: 8,192 points per cloud, source-only perturbations, fixed pose conventions, and unified metrics. Crucially, C3VDReg enables a systematic investigation of failure modes. We find that high geometric overlap alone is insufficient for reliable pose recovery: despite 74.3-93.1% ground-truth overlap, registration recall remains low across all evaluated methods. Through overlap, pose error, and translation decomposition analyses, we identify translation ambiguity along repetitive tubular anatomy as the primary bottleneck. This challenges the common assumption that increasing overlap or correspondence quality guarantees accurate registration, highlighting the need for stronger anatomical and contextual constraints. Code, model checkpoints, and data are available at this https URL . Comments: 15 pages, 6 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) MSC classes: 68T07 (Primary) 68T45, 92C55 (Secondary) Cite as: arXiv:2511.00260 [cs.CV] (or arXiv:2511.00260v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2511.00260 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Linzhe Jiang [ view email ] [v1] Fri, 31 Oct 2025 21:14:25 UTC (2,881 KB) [v2] Tue, 25 Aug 2026 00:07:03 UTC (5,120 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2025-11 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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 (opens in new tab) (opens in new tab) (opens in new tab)

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