3D Reconstruction Based On Pair Camera and Projector with Sub-Pixel Accuracy

In this paper, 3D reconstruction is done by using fringe projection profilometry (FPP). Fringe pattern is analyzed by using inverse function analysis (IFA) that is mathematically prove to improve accuracy compare to traditional methods. However, it known that IFA applied polynomial fittings which suffer from Runge phenomenon due to high degree polynomial used. Thus, this paper will introduce spline fitting in IFA method for 3D reconstruction to avoid Runge phenomenon. Thus, simulation by using MATLAB will be done to prove the ability of this method to produce 3D reconstruction with better accuracy compare to normal IFA

Improved method for phase wraps reduction in profilometry

In order to completely eliminate, or greatly reduce the number of phase wraps in 2D wrapped phase map, Gdeisat et al. proposed an algorithm, which uses shifting the spectrum towards the origin. But the spectrum can be shifted only by an integer number, meaning that the phase wraps reduction is often not optimal. In addition, Gdeisat's method will take much time to make the Fourier transform, inverse Fourier transform, select and shift the spectral components. In view of the above problems, we proposed an improved method for phase wraps elimination or reduction. First, the wrapped phase map is padded with zeros, the carrier frequency of the projected fringe is determined by high resolution, which can be used as the moving distance of the spectrum. And then realize frequency shift in spatial domain. So it not only can enable the spectrum to be shifted by a rational number when the carrier frequency is not an integer number, but also reduce the execution time. Finally, the experimental results demonstrated that the proposed method is feasible.Comment: 16 pages, 15 figures, 1 table. arXiv admin note: text overlap with arXiv:1604.0723

Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even in the presence of different types of error sources, e.g., intensity noise, low fringe modulation, and projector nonlinearity. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU

Implementation of spatial shift estimation approach for 3D profilometry based on digital fringe projection

Fringe Pattern Profilometry (FPP) based on Digital Fringe Projection (DFP) is a promising optical noncontact three-dimension (3D) profile measurement technologies due to its accuracy and flexibility. Popular FPP approaches retrieve the 3D profile information using the detection of phase difference, called the Phase Difference Estimation (PDE). Recently, a new kind of FPP approach, referred to as Spatial Shift Estimation (SSE) is introduced, which retrieves the 3D profile information using the detection of spatial shift instead of phase different. Compared with PDE approaches, SSE approaches are advantageous in that the projected fringe patterns do not need to be sinusoidal, and thus accurate reconstruction can be obtained even when nonlinear distortions exist on the fringe patterns. However, efficient implementation of SSE approaches is still an issue. This thesis work aims to implement the SSE approach for 3D profile measurement based on digital fringe projection. Firstly, a DFP system is designed and adopted in our laboratory, which is utilized as an experiment platform for the work presented in this thesis. SSE approaches are implemented on the system. Some problems associated with the implementation are studied and solved, including elimination of noise and distortion in the fringe patterns. Furthermore, an improved Inverse Function based Shift Estimation (IFSE) method is proposed to improve the performance of SSE approaches. Secondly, shift unwrapping problem associated with SSE is investigated. Through reviewing the phase unwrapping problem in PDE based FPP, we indicate that a similar shift unwrapping problem also exists in SSE approaches. A method for solving the problem has been proposed and the experiment results are presented to demonstrate the effectiveness of the proposed method. Finally, the research is carried out to improve the efficiency of SSE approaches. SSE approaches have the advantages that the projected fringe patterns are no longer required to be sinusoidal nor periodic. Therefore, we can choose a fringe pattern which has strong counter-interference capability against the noise and nonlinear distortion with simple implementation. Based on analysis of the limitations of traditional sinusoidal fringe, we propose to use sawtooth fringe pattern. Theoretical analysis has been given to evaluate the complexity of the proposed sawtooth fringe pattern based algorithms, and practical experiment are performed at last to prove the efficiency of this proposed fringe pattern

Shift estimation method based fringe pattern profilometry and performance comparison

In this paper, we present and study two approaches to fringe pattern profilometry (FPP) technique. Based on generalized analysis model for fringe pattern profilometry (FPP), Inverse Function based Shift Estimation (IFSE) and Gradient-based Shift Estimation (GSE) are proposed to calculate the shift between the projected and deformed fringe patterns. Further, computer simulations are utilized to compare the performance between these two methods. Meanwhile, we also compare these two algorithms with Phase Shift profilometry (PSP). It can be seen that both of these two shift estimation algorithms can significantly improve the measurement accuracy when the fringe patterns are nonlinearly distorted

Fourier Transform Profilometry in LabVIEW

Fourier transform profilometry (FTP) is an established non-contact method for 3D sensing in many scientific and industrial applications, such as quality control and biomedical imaging. This phase-based technique has the advantages of high resolution and noise robustness compared to intensity-based approaches. In FTP, a sinusoidal grating is projected onto the surface of an object, the shape information is encoded into a deformed fringe pattern recorded by a camera. The object shape is decoded by calculating the Fourier transform, filtering in the spatial frequency domain, and calculating the inverse Fourier transform; afterward, a conversion of the measured phase to object height is carried out. FTP has been extensively studied and extended for achieving better slope measurement, better separation of height information from noise, and robustness to discontinuities in the fringe pattern. Most of the literature on FTP disregards the software implementation aspects. In this chapter, we return to the basics of FTP and explain in detail the software implementation in LabVIEW, one of the most used data acquisition platforms in engineering. We show results on three applications for FTP in 3D metrology

A single-shot line-scanning spatially dispersed short coherence interferometer using Fourier transform profilometry

Single-shot inspection at nanoscale resolution is a problematic challenge for providing on-line inspection of manufacturing techniques such as roll-to-roll processes where the measurand is constantly moving. An example of such a measurement challenge is defect detection on vapor barrier films formed by depositing an aluminum oxide layer several tens of nanometres thick on a flexible polymer substrate. Effective detection and characterisation of defects in this layer requires a single-shot approach with nanometre scale vertical resolution. This paper describes a line-scanning interferometer where a short coherence light source having a 25 nm linewidth source is spatially dispersed across the measurand thus encoding spatial position along a profile by wavelength. Phase shift interferometry (PSI) can be used to decode phase and thus height information, but requires multiple image captures. In order to realise single-shot measurement which is more suitable for online applications, a Fourier transform profilometry (FTP) approach is necessary. This paper explores the implementation of the FTP approach and presents a comparison of the measurement capability of FTP with the previously reported PSI method

Fringe quality map for fringe projection profilometry in LabVIEW

The phase retrieval process is mainly affected by local shadows, irregular surface brightness and fringe discontinuities. To overcome these problems, image-processing strategies are carried out such as binary masks, interpolation techniques, and filtering. Similarly, many unwrapping algorithms have been developed to handle phase unwrapping errors in two-dimensional regions. The presence of error-prone areas can be visualized during the acquisition stage avoiding the use of image processing strategies and sophisticated phase unwrapping algorithms, which in many cases represent high computational costs and long execution times. To help overcome these problems, we propose a Fringe Quality Map based on a phase residue analysis to estimate error-prone areas during acquisition. The software was fully implemented in LabVIEW, and we provide the software as supplementary material. Experimental results demonstrate that the proposed method estimates areas with poor contrast, which lead to unwrapping errors, as well as phase errors in a more complex 3D shape. © Sociedad Española de Óptica