In spite of the recent advances in 3D shape measurement and geometry reconstruction, simultaneously achieving fast-speed and high-accuracy performance remains a big challenge in practice. In this paper, a 3D imaging and shape measurement system is presented to tackle such a challenge. The fringe-projection-profilometry-based system employs a number of advanced approaches, such as: composition of phase-shifted fringe patterns, externally triggered synchronization of system components, generalized system setup, ultrafast phase-unwrapping algorithm, flexible system calibration method, robust gamma correction scheme, multithread computation and processing, and graphics-processing-unit-based image display. Experiments have shown that the proposed system can acquire and display high-quality 3D reconstructed images and/or video stream at a speed of 45 frames per second with relative accuracy of 0.04% or at a reduced speed of 22.5 frames per second with enhanced accuracy of 0.01%. The 3D imaging and shape measurement system shows great promise of satisfying the ever-increasing demands of scientific and engineering applications.
3D shape measurement has emerged as a very useful tool in numerous fields because of its wide and ever-increasing applications. In this paper, we present a passive, fast and accurate 3D shape measurement technique using stereo vision approach. The technique first employs a scale-invariant feature transform algorithm to detect point matches at a number of discrete locations despite the discontinuities in the images. Then an automated image registration algorithm is applied to find full-field point matches with subpixel accuracy. After that, the 3D shapes of the objects can be reconstructed according to the obtained point matching and the camera information. The proposed technique is capable of performing a full-field 3D shape measurement with high accuracy even in the presence of discontinuities and multiple separate regions. The validity is verified by experiments.
A challenging task that has hampered the fully automatic processing of the digital image correlation (DIC) technique is the initial guess when large deformation and rotation are present. In this paper, a robust scheme combining the concepts of a scale‐invariant feature transform (SIFT) algorithm and an improved random sample consensus (iRANSAC) algorithm is employed to conduct an automated fast initial guess for the DIC technique. The scale‐invariant feature transform algorithm can detect a certain number of matching points from two images even though the corresponding deformation and rotation are large or the images have periodic and identical patterns. After removing the wrong matches with the improved random sample consensus algorithm, the three pairs of closest and non‐collinear matching points serve for the purpose of initial guess calculation. The validity of the technique is demonstrated by both computer simulation and real experiment.
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