Comparative Analysis of Warp Function for Digital Image Correlation-Based Accurate Single-Shot 3D Shape Measurement

Area of Science:

• Optical Metrology
• Computer Vision
• Mechanical Engineering

Background:

• Digital Image Correlation (DIC) is a high-precision, single-shot 3D shape measurement technique.
• Its efficiency and accuracy depend on warp function selection, subset size, and convergence criteria.
• Understanding these parameters is crucial for optimizing DIC performance.

Purpose of the Study:

• To comparatively analyze first-order and second-order warp functions in DIC-based 3D shape measurement.
• To investigate the impact of subset size and convergence criteria on DIC accuracy and efficiency.
• To provide guidance for selecting optimal parameters for different 3D shapes.

Main Methods:

• Utilized the inverse compositional Gauss-Newton (IC-GN) algorithm for DIC.
• Conducted comparative analysis of first-order and second-order warp functions.
• Performed both simulation tests and real experiments to evaluate performance.
• Examined the effects of varying subset sizes and convergence criteria.

Main Results:

• The study quantifies the influence of subset size and convergence criteria on DIC accuracy and efficiency for both warp function types.
• Identified key differences and similarities in parameter impacts between first-order and second-order warp functions.
• Demonstrated how parameter choices affect 3D shape measurement precision.

Conclusions:

• Established reference standards for selecting warp functions and setting convergence criteria in DIC.
• Provided insights into optimizing subset size for different warp functions and shapes.
• Aimed to enhance the efficiency and accuracy of DIC-based 3D shape measurement.