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

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.