KEYWORDS: Fringe analysis, Super resolution, 3D metrology, Education and training, Cameras, 3D projection, Convolution, Tunable filters, 3D image processing, Visualization
Fringe projection profilometry (FPP) is one of the most widely used optical three-dimensional (3D) perceiving techniques. However, in the realm of indoor 3D perceiving, achieving high-resolution data proves challenging due to the inherent trade-off between sampling resolution and measurement scale. This paper introduces an adaptive-resolution-based method to address this challenge. Specifically, the approach leverages the super-resolution reconstruction technique to enhance the resolution of captured fringe patterns, where an end-to-end fringe pattern super-resolution network (FPSRNet) is constructed to adaptively achieve different super-resolution values. The desired high-resolution 3D results can be reconstructed from these new patterns. Experimental results verify the effectiveness of the proposed method in indoor 3D perceiving.
Simultaneous Localization and Mapping (SLAM) has been widely used for indoor building information modeling (BIM) applications. However, the data perceived by traditional sensors is not satisfactory, which also limits the capabilities of SLAM. Recently, high-quality fringe projection profilometry (FPP) sensor has been introduced into SLAM for indoor applications. In this paper, we first introduce the workflow of FPP-SLAM and then verify FPP-SLAM on BIM of cultural heritages.
Recently, Mueller matrix polarimetry has been widely used in a number of aspects, such as biomedicine, remote sensing, target decomposition, etc. However, it is still challenging for the measurement, decomposition, and depolarization resulting from complex light-matter interactions of mixed samples. In this work, the Mueller matrix measurement and decomposition methods are proposed for the mixed sample with rough surfaces. First, the linear combination of the two different Mueller matrices resulting in depolarization is proved. Second, the optimal method is used to decompose and correct the Mueller matrices of the sample. Finally, the accurate depolarization of the mixed sample is obtained by using the eigenvalue method. The experiment results validate that the method has great potential in the accurate measurement, noise reduction, and decomposition of Mueller matrices of mixed samples with rough surfaces.
Panoramic 3D measurement becomes increasingly important for fringe projection profilometry (FPP). Traditional physical markers-assisted (PMA) method suffers from inefficiencies and non-complete measurement. An optical markers-assisted (OMA) panoramic 3D method has been recently proposed, which enables accurate, efficient and non-destructive panoramic 3D measurement. In this paper, we give a comprehensive comparison between OMA and PMA, which provides reasonable suggestions for different panoramic 3D measurement applications.
In the recording process of phase-shifting profilometry, intensity fluctuation caused by uorescent light source instability may occur and then introduce a non-ignorable phase error. More importantly, the selection of sampling speed will also affect the value of the phase error, which even up to 0.12 rad. To suppress this problem, a deep learning-based fluorescent light error suppression (DLFLES) method is proposed to achieve high-precise measurement under fluorescent light. Experiments demonstrate that the shapes of the reconstructed 3-D images are more precise using the proposed method. Our research would promote the development of accurate 3-D measurement under the interference of external light sources by using deep learning.
Fringe projection profilometry (i.e., FPP) has been one of the most popular techniques in three-dimensional (i.e., 3-D) measurement. In FPP, it is necessary to obtain accurate desired phase by using a small number of fringes in dynamic measurement. Recently, fringe pattern transformation method (i.e., FPTM) is proposed based on deep learning, which can achieve accurate 3-D measurement using a single fringe, but the phase error is still higher than the phase-shifting algorithm. In this paper, the phase error of FPTM is analyzed and the relationship between it and local depth change rate is illustrated firstly. Then, the accuracy of FPTM can be improved by using more fringes. Compared with traditional methods, FPTM can achieve higher precision 3-D measurement when less fringes are used.
The light scattering brings serious degradation for the object information. The conventional optical techniques cannot extract the relevant message on the object location in the scattering. In this paper, in the phase-space, the speckle characteristic with different depths has been analyzed and discussed. We utilize the phase-space-prior to locate the objects through a strong scattering medium with a learning method. Comparing with the single data-driven method, our scheme can help the deep neural network (DNN) to extract the depth information efficiently. The experimental results proved that our method is novel and technically correct with high locating accuracy. Our technique paves the way to a physical-informed DNN in locating and ranging objects through complex scattering media.
Microscopes are now widely used in the field of three-dimensional measurements for their high magnification and changeable physical focal length. However, the small depth of focus restricts the camera from taking clear images with apparent tilts. To set the camera in focus, the calibration board should be near-parallel to the imaging plane. As a result, the traditional calibration methods are not practical for a normal lens, which requires multiple views of the calibration board. For this, a method in which a digital camera with a microscope is calibrated precisely under the near-parallel condition is proposed. In this method, the magnification rate of the imaging system, which is equal to the rate of image distance to object distance, is calculated precisely and utilized to calculate camera parameters. Due to the invariance of the magnification rate, the parameters calculated by this method are accurate and stable. In addition, to solve the problem that the physical focal length cannot be calibrated by one image, the magnification method is adopted to measure it. Finally, the stability and accuracy are evaluated by simulations and experiments.
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