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Digital volume correlation (DVC) is a powerful and widely used technique for measuring the internal 3D deformation field of a wide range of materials. One of the most popular DVC algorithms is the reliability-guided DVC (RG-DVC) which is good at dealing with large continuous deformation. However, RG-DVC requires a manually specified seed from which computation starts, and suffers from the efficiency due to a huge amount of computation and data dependency. This paper proposes a GPU accelerated parallel reliability-guided DVC algorithm (CuSIFT-RGDVC) on CUDA, which leverages 3D scale-invariant feature transform (3D SIFT) to assist seed selection to realize fully automation and improves performance utilizing GPU computing. In CuSIFT-RGDVC, reliability-guided displacement tracking (RGDT) is rewritten using sorted array-based batch processing mechanism which is a globally sequential locally parallel model, and multi-granularity parallelism is adopted to maximize GPU utilization. The empirical result shows that the proposed CuSIFT-RGDVC provides up to 29.1x speedup compared with our multi-threaded implementation and achieves the same level of computation speed as the state-of-the-art path-independent DVC without sacrificing accuracy.

A novel path independent digital volume correlation (DVC) method is proposed, in which the internal deformation field is determined with the aid of the features extracted from the volume images. The deformation vector at each point of interest is first estimated by tracking the motion of the nearby keypoints obtained through 3D scale-invariant feature transform (SIFT), and then fed as the initial guess into the 3D inverse compositional Gauss-Newton algorithm to achieve high accuracy result. Benefiting from the robustness of SIFT to various deformation and distortion of image, the proposed DVC method demonstrates outstanding performance to automatically process the volume images containing large and complex deformation, which keep challenging to current DVC methods for decades. The proposed DVC method is further powered by the parallel computing on GPU. An ultrafast computation speed (about 10,000 POI/s) can be reached on a personal computer when dealing with the volume images of normal sizes. The 3D SIFT aided DVC method, which achieves unprecedented balance between accuracy, adaptability, and efficiency, shows great potential in the quantitative analysis of internal deformation.

Current iterative digital image correlation (DIC) algorithms can efficiently converge at the deformation vector with high accuracy when they are fed with reliable initial guess. Thus, the adaptability of DIC method is dominated to a large extent by the estimation of initial guess. In recent years, image feature-based technique, especially the scale-invariant feature transform (SIFT), was introduced to DIC for the estimation of initial guess in the case of large and complex deformation, due to its robustness in handling the images with translation, rotation, scaling, and localized distortion. However, feature extraction and matching in SIFT are very time consuming, which limits the applications of the SIFT-aided DIC. In this study, we developed a SIFT-aided path-independent DIC method and accelerated it by introducing the parallel computing on graphics processing unit (GPU) or multi-core CPU. In our method, SIFT features are used to estimate the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI). The experimental study shows that the developed method can deal with large and inhomogeneous deformation with high accuracy. Parallel computing (especially on GPU) accelerates significantly the proposed DIC method. The achieved computation speed satisfies the need for real-time processing with high resolution for the images of normal sizes.