MeMVSNet: Monocular Depth Enhanced Multi-view Reconstruction

MeMVSNet: Monocular Depth Enhanced Multi-view Reconstruction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MeMVSNet: Monocular Depth Enhanced Multi-view Reconstruction Cui Haohao, Di Yanqiang, Meng Xianguo, Feng Shaochong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303998/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Multi-view stereo networks typically build a multi-view cost volume to regress the depth values. Previous methods introduce finely designed network structures for feature extraction to promote the quality of cost volume. However extracted features can be indistinguishable in texture-less region, reflective surfaces etc. To this end, we propose MeMVSNet, a novel framework for fusing monocular depth values to enhance the feature extraction. In particular, we utilize a two-branch feature fusion network to extract the geometry clues from monocular depth predictions to enrich the information in image features. In order to eliminate the influence of scalar factor, monocular depth predictions are normalized first. The proposed method achieves competitive performance on DTU and Tanks an Temples(T&T). Qualitative evaluation demonstrates that our method is more robust in challenging scenes. Multi-view stereo Monocular depth Feature fusion Two-branch network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 15 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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