A three-dimensional (3D) reconstruction method of structured light based on photoelastic fringes was proposed in this research. The photoelastic fringes are produced by both simulation and a polycarbonate disk under diametric compression load. Six fringes are projected onto an object by using the six-step phase shifting technique. Therefore, the isochromatic phase image is calculated. After phase unwrapping, the isochromatic phase image can be used for 3D reconstruction. In order to verify the effectiveness of this method, two experiment devices were built by using projector and photoelastic instrument, respectively. The results show that the fringe pattern based on photoelasticity can be used for 3D reconstruction as a structured light pattern. Compared to the simulation results, the fringes produced by load are more blurred. In order to obtain a better reconstruction result, a large load should be applied to produce dense fringes.
The similar analysis of time sequence images to achieve image matching is a foundation of tasks in dynamic environments, such as multi-object tracking and dynamic gesture recognition. Therefore, we propose a matching method of time sequence images based on the Siamese network. Inspired by comparative learning, two different comparative parts are designed and embedded in the network. The first part makes a comparison between the input image pairs to generate the correlation matrix. The second part compares the correlation matrix, which is the output of the first comparison part, with a template, in order to calculate the similarity. The improved loss function is used to constrain the image matching and similarity calculation. After experimental verification, we found that it not only performs better, but also has some ability to estimate the camera pose.
We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.
Residual stresses are important index to evaluate the precision molded glass plane. Residual stresses inside a glass cylinder was simulated by Finite Element Method. The circular polariscope was used to compare retardation and isoclinic angle between the simulated residual stresses and the thermal treated glass cylinder. The retardation and isoclinic angle of simulated residual stresses were calculated by Jones calculus. The comparison results show a good agreement.
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