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

Published

May 13, 2026

Author(s)

Zixiang Tong, Yujie Zhang, Edward Ando, Bin Chen, Brendan Croom, John Dabiri, Christian Franck, Matthew Fu, Helena Jin, Orion Kafka, Sriram Kunnoth, Thao Nguyen, Jacob Notbohm, Mohak Patel, Mainak Sarkar, Angkur Shaikeea, Jing Zhang, Alexander Landauer, Jin Yang

Abstract

Background: Digital Volume Correlation (DVC) is a powerful experimental technique for quantifying 3D full-field volumetric displacements and strains. In light of its increase adoption in metrological applications, there is a critical need for benchmark datasets to systematically evaluate and compare the performance of various DVC algorithms across different materials, imaging modalities, and deformation scenarios. Objective: Building on the foundations of DVC Challenge 1.0, this initiative, DVC Challenge 2.0, aims to create a repository of benchmark DVC datasets to provision researchers worldwide with useful data validate, and refine their DVC algorithms. This can help 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 pairs and series 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 challenges, such as complex deformation fields, poor image quality, and anisotropic or sparse speckle patterns. All datasets are compiled into a data framework with a uniform format and made openly accessible. 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.

Citation

Experimental Mechanics

Pub Type

Journals

Keywords

Digital Volume Correlation, Benchmark dataset, Micro-X-ray computed tomography, Confocal microscopy, Synthetic image generation, Neutron imaging, Uncertainty quantification

Citation

Tong, Z. , Zhang, Y. , Ando, E. , Chen, B. , Croom, B. , Dabiri, J. , Franck, C. , Fu, M. , Jin, H. , Kafka, O. , Kunnoth, S. , Nguyen, T. , Notbohm, J. , Patel, M. , Sarkar, M. , Shaikeea, A. , Zhang, J. , Landauer, A. and Yang, J. (2026), Digital Volume Correlation Challenge 2.0: A Comprehensive Dataset for Digital Volume Correlation Benchmarking, Experimental Mechanics (Accessed May 14, 2026)

Additional citation formats