Abstract
Multisensor systems can overcome the limitation of measurement range of single-sensor systems, but often require complex calibration and data fusion. In this study, a three-dimensional (3D) measurement method of four-view stereo vision based on Gaussian process (GP) regression is proposed. Two sets of point cloud data of the measured object are obtained by gray-code phase-shifting technique. On the basis of the characteristics of the measured object, specific composite kernel functions are designed to obtain the initial GP model. In view of the difference of noise in each group of point cloud data, the weight idea is introduced to optimize the GP model, which is the data fusion based on Bayesian inference method for point cloud data. The proposed method does not require strict hardware constraints. Simulations for the curve and the high-order surface and experiments of complex 3D objects have been designed to compare the reconstructing accuracy of the proposed method and the traditional methods. The results show that the proposed method is superior to the traditional methods in measurement accuracy and reconstruction effect.
Highlights
The combination of structural illumination and stereo vision has recently provided increased possibilities for three-dimensional (3D) object measurement, robot vision, and mechanical device control [1,2,3]
Stereo vision measurement methods can be divided into monocular stereo, binocular stereo, and multivision stereo (MVS) methods [4,5,6]
For traditional multisensor working independently in its system, Wu et al presented a flexible 3D reconstruction method based on phase matching, which reduced the complexity of calibration between single-sensor systems [7]
Summary
The combination of structural illumination and stereo vision has recently provided increased possibilities for three-dimensional (3D) object measurement, robot vision, and mechanical device control [1,2,3]. Stereo vision measurement methods can be divided into monocular stereo, binocular stereo, and multivision stereo (MVS) methods [4,5,6]. The measurement plan of four-view stereo measurement method in this study is one of the applications of MVS technology. Xue et al presented an improved patch-based multiview stereo method by introducing a photometric discrepancy function based on a DAISY descriptor; this method obtains good reconstruction results in occlusion and edge regions of large-scale scenes [8]. Zhang et al created a multiview stereo vision system for true 3D reconstruction, modeling, and phenotyping of plants. This system yielded satisfactory 3D reconstruction results and demonstrated the capability to study plant development where the same plants were repeatedly
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