KEYWORDS: Depth maps, Structured light, Education and training, 3D image processing, Phase reconstruction, Lithium, Fringe analysis, Signal processing, 3D modeling, 3D metrology, Deep learning
In recent years, end-to-end depth map prediction from single-shot fringe modulation images in structured light 3D measurement(F2D) has drawn widespread attention, which has significantly reduced measurement times and eliminated the complex intermediate steps in the traditional method. However, F2D is a long-distance ill-conditioned prediction problem, and it is difficult for existing regression networks to achieve high-precision pixel-by-pixel prediction over long distances in space and time. For the challenge, we propose APS-UNet(Absolute Phase aided Supervision UNet), an endto- end depth maps prediction network supervised by an absolute phase branch. With the core physical process, absolute phase branch as auxiliary supervision, can decompose the one challenging long-distance prediction into two easier shortdistance prediction tasks. Moreover, in the training process, the two branches provide feedback to each other, enhancing the accuracy and robustness of depth prediction. Compared to Res-UNet, APS-UNet demonstrates a 32% decrease in mean absolute error (MAE) based on the real dataset, highlighting the effectiveness of this network.
KEYWORDS: Semantics, 3D mask effects, 3D metrology, Structured light, Shadows, 3D modeling, Education and training, Deep learning, Image processing, Feature extraction
Deep learning-driven structured light 3D measurement has garnered significant attention due to the fast speed, high precision and non-contact characteristic. However, the accurate prediction of edge discontinuity area is still one of the challenges. In single-frame end-to-end absolute phase prediction task, we initially proposed a mask semantic attention network (MSAN) to enhance the edge and whole accuracy. Firstly, mask serves to partition the scene into its background (shadow) and foreground (objects) elements, and it provides semantic attention for the network. Secondly, we designed a mask fusion (MF) module which can effectively integrates feature maps with mask semantics. Based on the MF module and mask semantic information, we developed a U-shaped network architecture, and each layer feature map of the decoder is fused with the input mask adopting the MF module. MSAN improves edge prediction accuracy by explicitly identifying edge regions and drawing the network's attention to the edges and objects rather than shadow areas, enhancing overall prediction accuracy. Validation on real datasets showed that the mean absolute error decreased by 33% and the root mean square error decreased by 76% with MSAN, demonstrating the network's capability to improve both overall and edge precision in structured light deep learning tasks. This advancement significantly benefits the development of high precise and rapid structured light 3D measurement technologies.
KEYWORDS: Phase unwrapping, Deep learning, 3D metrology, Shadows, Semantics, Network architectures, Education and training, Visualization, Time metrology, Phase reconstruction
Single-frame high-precision 3D measurement using deep learning has been widely studied for its minimal measurement time. However, the long physical and semantic distances make the end-to-end absolute phase reconstruction of single-frame grating challenging. To tackle this difficulty, we propose the DSAS-S2AP-X (Dual-Stage Auxiliary Supervision Network for Single-Frame to Absolute Phase Prediction with X) strategy, which includes the secondary highest frequency unwrapped phase and the highest frequency wrapped phase supervision branches. It combines a multi-frequency temporal phase unwrapping model with existing regression networks X (meaning arbitrary). Experimental results have shown that the DSAS-S2AP-ResUNet34 strategy can reduce the mean absolute error (MAE) and root mean square error (RMSE) of the absolute phase by 34.3% and 25.9% respectively based on the ResUNet34.
The three-frequency heterodyne phase shift profilometry is widely used in high-precision 3D reconstruction. However, the high accuracy comes at the cost of requiring many projected frames, which increases measurement time and decreases measurement efficiency. To address this challenge, we propose a rapid, high-precision absolute phase acquisition method called X+1+1, which fully integrates the accuracy advantages of the multi-frequency n-step heterodyne phase-shifting method and the speed advantages of the Modified Fourier transform profilometry (MFTP). The highest frequency gratings use the standard X-step phase-shifting method to determine the wrapped phase, ensuring high unwrapping accuracy and obtaining background light intensity. For intermediate and low frequencies, a single-frame grating and the Backgroundgenerated Modified Fourier transform profilometry (BGMFTP) are used to solve each wrapped phase to reduce the measurement time. Finally, the heterodyne method processes these three-frequency wrapped phases to obtain the absolute phase. Experimental results demonstrated the high accuracy and speed of this method in the 3D measurement process. Compared to traditional Fourier transform profilometry, the X+1+1 method has a 53% improvement in accuracy, while maintains the same level of performance as the three-frequency four-step heterodyne method in continuous non-marginal flat areas and the projection time was reduced by approximately 50%. The proposed X+1+1 method provides a new solution for balancing speed and accuracy in the application and promotion of structured-light 3D measurement.
The pharmaceutical industry extensively employs glass vials for the packaging of sterile preparations. Air invasion resulted from vial leakage leads to preparation quality deterioration. Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been established as an effective non-contact method for assessing seal quality by detecting residual oxygen concentration in vial headspace. However, definitely unlike that the scheme of cavity-enhanced absorption spectroscopy (CEAS) has a sufficiently long optical path, headspace oxygen detection should be realized within the short inner diameter length of vials, while the external optical path is longer and with rich oxygen in the open production environment. Innovatively, we attempt to make full use of the cavity-like geometric nature of the glass vial to increase the inner absorption optical path length, by coating a high-reflectance silver ring film on the outer wall of vials. This novel scheme enables the incident laser to achieve Axial Section Multiple Reflection (ASMR) within space-limited vials (using ‘n-ASMR’ denotes the mode with ‘n’ times of reflections), extending the absorption path effectively without equipping any additional absorption cavity, we name it Cavity-Like Enhanced Absorption Spectroscopy (CLEAS), which breakthroughs the limitations of the conventional Direct Transmission (DT) method only along the diameter direction. In the Allan variance analysis tests, compare with the detection limit 0.226% with an integration time 33.8s of the DT method, our 2/4/6/8-ASMR methods achieve the detection limits 0.058%, 0.054%, 0.058% and 0.046% with integration time 28.9s, 14.6s, 4.76s and 5.60s, respectively, which indicate a brand-new roadmap has been discovered by the CLEAS scheme to extend absorption path in space-limited glass vial without increasing any hardware facilities.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.