In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.
Fringe projection profilometry (FPP) is one of the most popular three-dimensional (3D) shape measurement techniques, and has becoming more prevalently adopted in intelligent manufacturing, defect detection and some other important applications. In FPP, how to efficiently recover the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional fringe patterns, which maximizes the efficiency of the retrieval of absolute phase. Inspired by the recent success of deep learning technologies for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies the phase retrieval, geometric constraints, and phase unwrapping steps into a comprehensive framework. Driven by extensive training dataset, the neutral network can gradually "learn" how to transfer one high-frequency fringe pattern into the "physically meaningful", and "most likely" absolute phase, instead of "step by step" as in convention approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved based on only single-frame projection. Experimental results demonstrate that compared with traditional SPU, our method can more efficiently and stably unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views. Limitations about the proposed approach are also discussed. We believe the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated object from a single fringe pattern.
In recent years, fringe projection has become an established and essential method for dynamic three-dimensional (3-D) shape measurement in different fields such as online inspection and real-time quality control. Numerous high-speed 3-D shape measurement methods have been developed by either employing high-speed hardware, minimizing the number of pattern projection, or both. However, dynamic 3-D shape measurement of arbitrarily-shaped objects with full sensor resolution without the necessity of additional pattern projections is still a big challenge. In this work, we introduce a high-speed 3-D shape measurement technique based on composite phase-shifting fringes and a multi-view system. The geometry constraint is adopted to search the corresponding points independently without additional images. Meanwhile, by analysing the 3-D position and the main wrapped phase of the corresponding point, pairs with an incorrect 3-D position or a considerable phase difference are effectively rejected. All of the qualified corresponding points are then corrected, and the unique one as well as the related period order is selected through the embedded triangular wave. Finally, considering that some points can only be captured by one of the cameras due to the occlusions, these points may have different fringe orders in the two views, so a left-right consistency check is employed to eliminate those erroneous period orders in this case. Several experiments on both static and dynamic scenes are performed, verifying that our method can achieve a speed of 120 frames per second (fps) with 25-period fringe patterns for fast, dense, and accurate 3-D measurement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.