Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning

Abstract

Fringe projection profilometry (FPP) has become increasingly important in dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of the measured object before shape profiling. However, traditional phase retrieval techniques often require a large number of fringes, which may generate motion-induced error for dynamic objects. In this paper, a novel phase retrieval technique based on deep learning is proposed, which uses an end-to-end deep convolution neural network to transform a single or two fringes into the phase retrieval required fringes. When the object’s surface is located in a restricted depth, the presented network only requires a single fringe as the input, which otherwise requires two fringes in an unrestricted depth. The proposed phase retrieval technique is first theoretically analyzed, and then numerically and experimentally verified on its applicability for dynamic 3-D measurement.

Three-Dimensional Morphology and Size Measurement of High-Temperature Metal Components Based on Machine Vision Technology: A Review

“…In 2020, Haotian Yu et al [ 67 ] built a sine computing neural network (FPTNet). This network contains two subnetworks, FPTNet-C and FPTNet-U, which perform phase calculation and phase expansion, respectively.…”

3D Reconstruction From Structured-Light Profilometry With Dual-Path Hybrid Network

“…In [16], two low-modulation patterns with different phase shifts are transformed into a set of three phase-shifted high-modulation fringes by using FMENet. [17] proposes a novel phase retrieval technique based on CNN, which uses an end-to-end deep convolution neural network to transform a single or two patterns into the phase retrieval required patterns, and numerically and experimentally verified its applicability for dynamic 3D measurement. In [18], they employs UNet to transform a color structured-light pattern into multiple triple-frequency phase-shifted grayscale patterns, from which the 3D shape can be accurately reconstructed.…”

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

“…Through retrieving phase distributions from the captured images, the height or depth map can be determined and the 3D shape can be reconstructed. Inspired by Zhang's work [37] of encoding three fringe patterns of different frequencies into a single composite RGB image and a few other researchers' work [38,39] of using a deep learning method to transform one or two fringe images to multiple phase-shifted fringe images, the proposed technique aims to employ a convolutional neural network (CNN) model to transform a single-shot red-green-blue (RGB) fringe-pattern image into a number of multi-frequency phase-shifted grayscale fringe-pattern images, which are then used to reconstruct the depth and 3D shape map using the conventional FPP algorithm. Due to the main purpose of transforming a single fringe pattern into multiple fringe patterns, the CNN model is called a fringe-to-fringe network.…”

“…It is noteworthy that, unlike Yu's work [38], which uses multiple networks and one or multiple fringe images, the proposed technique uses a single network and a single image for fringe-to-fringe transformations. In addition, a favorable UNet-like network other than a simple autoencoder-like network is employed to enhance the transformation performance.…”