Initializing and accelerating Stereo-DIC computation using semi-global matching with geometric constraints

Stereo Digital Image Correlation (Stereo-DIC) has become a mainstream optical metrology technique for quantitatively analyzing full-field 3D shape, displacement, or deformation of materials and structures. Whether it is to measure 3D profile or deformation, stereo matching is essential for Stereo-DIC to reconstruct 3D point clouds from stereo images. Traditional feature-based (e.g., SIFT) methods provide initial 2D displacements for stereo matching with the aid of extracting massive features, but at the cost of expensive computational overhead. In addition, these methods preclude precise measurement of objects with steep and ridged surfaces or undergoing large rotation and/or deformation due to low feature matching accuracy of complex regions caused by perspective differences. In this paper, we propose a fast and robust stereo matching method using semi -global matching with geometric constraints (GC-SGM) for initializing and accelerating Stereo-DIC computation. For GC-SGM, an optimized semi-global matching (SGM) algorithm based on GPU acceleration is first utilized to quickly estimate dense and reliable disparity maps between the rectified stereo images. The global pixel -wise 2D correspondence between raw stereo images can be established inversely using epipolar constraints and 1D disparity information, and then converted to accurate and initial second-order deformation parameters for 2D-DIC-based sub-pixel refinement by least-squares-based surface fitting. Experimental results prove that the proposed GC-SGM enhances the matching correctness and robustness for complex objects while improving the processing speed on GPU by 3 similar to 10 times compared with SIFT-based methods, enabling high-precision and computationally efficient 3D shape and deformation measurement.

Automated summary

This paper proposes a fast and robust stereo matching method using geometric constraints for Stereo-DIC, which improves the accuracy and robustness of matching while speeding up the computation. Experimental results demonstrate the advantages of this method in terms of high precision and computational efficiency.