Digital Volume Correlation Challenge 2.0: A Comprehensive Dataset for Digital Volume Correlation Benchmarking

Digital Volume Correlation Challenge 2.0: A Comprehensive Dataset for Digital Volume Correlation Benchmarking | 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 Digital Volume Correlation Challenge 2.0: A Comprehensive Dataset for Digital Volume Correlation Benchmarking Zixiang Tong, Yujie Zhang, Edward Ando, Bin Chen, Brendan P. Croom, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9683321/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Digital Volume Correlation (DVC) is a powerful experimental technique for quantifying 3D full-field volumetric displacements and strains. In light of its increased adoption in metrological applications, there is a critical need for benchmark datasets to systematically evaluate the performance of various DVC algorithms across different materials, imaging modalities, and deformation scenarios. Objective: Building on the foundations of DVC Challenge 1.0, the DVC Challenge 2.0 initiative aims to create a repository of DVC datasets to enable researchers to validate and refine their DVC algorithms against common benchmarks. This can help in expanding the scope and performance of DVC and foster innovation in volumetric deformation measurement. Methods: DVC Challenge 2.0 compiles a diverse collection of volumetric image sets contributed by the global research community. These datasets encompass different materials, loading conditions, and imaging modalities, including confocal/multiphoton microscopy, X-ray computed tomography (XCT), neutron tomography, and synthetically generated images. These datasets present various metrological challenges, such as complex deformation fields, poor image quality, and anisotropic or sparse speckle patterns. All datasets are published in an open repository, with a uniform image format and a common data framework. Results: The resulting repository provides benchmark datasets for validating and comparing DVC algorithms, facilitating the exploration of DVC capabilities in diverse and challenging scenarios. Conclusion: By promoting collaboration and open data sharing, DVC Challenge 2.0 will drive innovation in volumetric deformation measurement techniques and broaden the impact of DVC. It will also help establish a baseline for comparison of DVC algorithms and codes. Digital Volume Correlation Benchmark dataset Micro-X-ray computed tomography Confocal microscopy Synthetic image generation Neutron imaging Uncertainty quantification Full Text Additional Declarations The authors declare no competing interests. Supplementary Files DVCChallenge2DatasetsSI20260507.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9683321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638507160,"identity":"982c8f9e-9c73-4625-af1e-ec9526388682","order_by":0,"name":"Zixiang Tong","email":"","orcid":"https://orcid.org/0009-0008-6807-0757","institution":"The University of Texas at Austin","correspondingAuthor":false,"prefix":"","firstName":"Zixiang","middleName":"","lastName":"Tong","suffix":""},{"id":638507161,"identity":"93688a04-2ef6-4c80-9dc0-2f627e07ecdc","order_by":1,"name":"Yujie Zhang","email":"","orcid":"","institution":"The University of Texas at Austin","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Zhang","suffix":""},{"id":638507162,"identity":"bbf4af43-bc69-43c0-9691-f88ad799c956","order_by":2,"name":"Edward Ando","email":"","orcid":"https://orcid.org/0000-0001-5509-5287","institution":"Ecole Polytechnique Federale de Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"","lastName":"Ando","suffix":""},{"id":638507163,"identity":"d2d7995b-3617-41d6-8f43-213d91bfa867","order_by":3,"name":"Bin Chen","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Chen","suffix":""},{"id":638507164,"identity":"b7028f50-8f50-40be-8179-2b889b4be5ae","order_by":4,"name":"Brendan P. 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In light of its increased adoption in metrological applications, there is a critical need for benchmark datasets to systematically evaluate the performance of various DVC algorithms across different materials, imaging modalities, and deformation scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eBuilding on the foundations of DVC Challenge 1.0, the DVC Challenge 2.0 initiative aims to create a repository of DVC datasets to enable researchers to validate and refine their DVC algorithms against common benchmarks. This can help in expanding the scope and performance of DVC and foster innovation in volumetric deformation measurement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e DVC Challenge 2.0 compiles a diverse collection of volumetric image sets contributed by the global research community. These datasets encompass different materials, loading conditions, and imaging modalities, including confocal/multiphoton microscopy, X-ray computed tomography (XCT), neutron tomography, and synthetically generated images. These datasets present various metrological challenges, such as complex deformation fields, poor image quality, and anisotropic or sparse speckle patterns. All datasets are published in an open repository, with a uniform image format and a common data framework.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The resulting repository provides benchmark datasets for validating and comparing DVC algorithms, facilitating the exploration of DVC capabilities in diverse and challenging scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e By promoting collaboration and open data sharing, DVC Challenge 2.0 will drive innovation in volumetric deformation measurement techniques and broaden the impact of DVC. It will also help establish a baseline for comparison of DVC algorithms and codes.\u003c/p\u003e","manuscriptTitle":"Digital Volume Correlation Challenge 2.0: A Comprehensive Dataset for Digital Volume Correlation Benchmarking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 02:51:40","doi":"10.21203/rs.3.rs-9683321/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0908b8a-81f6-4717-abe7-6d039c836d81","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T02:51:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 02:51:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9683321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9683321","identity":"rs-9683321","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}