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

Area of Science:

• Experimental Mechanics
• Metrology
• Computational Mechanics

Background:

• Digital Volume Correlation (DVC) quantifies 3D displacements and strains.
• Growing adoption in metrology necessitates benchmark datasets for algorithm evaluation.
• Existing methods lack standardized benchmarks for diverse materials and imaging.

Purpose of the Study:

• Establish DVC Challenge 2.0 as a benchmark repository for DVC algorithms.
• Enable systematic validation and refinement of DVC techniques.
• Foster innovation in volumetric deformation measurement.

Main Methods:

• Compiled diverse volumetric image datasets from global researchers.
• Included various materials, loading conditions, and imaging modalities (microscopy, XCT, neutron tomography).
• Addressed metrological challenges like complex deformations and poor image quality.

Main Results:

• Created a repository of benchmark datasets for DVC algorithm validation.
• Facilitated comparison of DVC algorithms across challenging scenarios.
• Provided a common framework for data analysis and comparison.

Conclusions:

• DVC Challenge 2.0 promotes collaboration and open data sharing.
• Drives innovation and broadens the impact of DVC techniques.
• Establishes a baseline for comparing DVC algorithms and codes.