Abstract
A sub-voxel digital volume correlation (DVC) method combining the inverse compositional Gauss-Newton (IC-GN) algorithm with the fast Fourier transform-based cross correlation (FFT-CC) algorithm is proposed to eliminate path-dependence in current iterative DVC methods caused by the initial guess transfer scheme. The proposed path-independent DVC method is implemented on NVIDIA compute unified device architecture (CUDA) for graphic processing unit (GPU) devices. Powered by parallel computing technology, the proposed DVC method achieves a significant improvement in computation speed on a common desktop computer equipped with a low-end graphics card, i.e. up to 23.3 times faster than the sequential implementation and 3.7 times faster than the multithreaded implementation of the same DVC method running on a 6-core CPU. This speedup has no compromise with resolution and accuracy. It benefits from the coarse-grained parallelism that the points of interest (POIs) are processed simultaneously and also from the fine-grained parallelism that the calculation at each POI is performed with multiple threads in GPU.
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