Binarized dual phase-shifting method for high-quality 3D shape measurement

Super-resolution phase retrieval network for single-pattern structured light 3D imaging

Structured light 3D imaging is often used for obtaining accurate 3D information via phase retrieval. Single-pattern structured light 3D imaging is much faster than multi-pattern versions. Current phase retrieval methods for single-pattern structured light 3D imaging are however not accurate enough. Besides, the projector resolution in a structured light 3D imaging system is expensive to improve due to hardware costs. To address the issues of low accuracy and low resolution of single-pattern structured light 3D imaging, this work proposes a super-resolution phase retrieval network (SRPRNet). Specifically, a phase-shifting module is proposed to extract multi-scale features with different phase shifts, and a refinement and super-resolution module is proposed to obtain refined and super-resolution phase components. After phase demodulation and unwrapping, high-resolution absolute phase is obtained. A sine shifting loss and a cosine shifting loss are also introduced to form the regularization term of the loss function. As far as can be ascertained, the proposed SRPRNet is the first network for super-resolution phase retrieval by using a single pattern, and it can also be used for standard-resolution phase retrieval. Experimental results on three datasets show that SRPRNet achieves state-of-the-art performance on 1×, 2×, and 4× super-resolution phase retrieval tasks.

Passive binary defocusing for large depth 3D measurement based on deep learning

Phase-shifting profilometry (PSP) based on the binary defocusing technique has been widely used due to its high-speed capability. However, the required adjustment in projector defocus by traditional method is inaccurate, inflexible, and associated with fringe pitch. Instead of manual defocusing adjustment, a passive defocus of the binary patterns based on deep learning is proposed in this paper. Learning the corresponding binary patterns with a specifically designed convolutional neural network, high-quality three-step sinusoidal patterns can be generated. Experimental results demonstrate that the proposed method could reduce phase error by 80%-90% for different fringe pitches without projector defocus and outperform the traditional method by providing more accurate and robust results within a large measuring depth.

End-to-end pattern optimization technology for 3D shape measurement

The nonlinear errors caused by the gamma effect seriously affect the measurement accuracy of phase-measuring profilometry systems. We present a novel robust end-to-end pattern optimization technology for phase-height mapping. This method generates standard sinusoidal patterns on the reference plane on a camera image instead of on a projection image by optimizing the projection image, which can avoid phase errors due to phase conversion from projector to camera. The pixel set mapping is achieved by chessboard calibration; then the projection image is optimized with the multiscale optimization method and the dislocation optimization method based on proportional integral derivative control. The experiments show that the proposed method can effectively avoid the influence of nonlinear effect and achieve high-quality three-dimensional measurement.

Defocused projection model for phase-shifting profilometry with a large depth range

Phase-shifting 3D profilometry is widely combined with defocused projection, but the accuracy of defocused projection could be far below expectations especially in the case of large depth range measurement. In this paper, a new defocus-induced error related to the shape of the measured object is pinpointed and a novel defocused projection model is established to cope with such a error to improve the accuracy of defocusing phase-shifting profilometry. Supplemented with a specialized calibration and reconstruction procedure, the phase is well corrected to obtain accurate measurement results. Furthermore, the impact of the defocus-induced error is analyzed through simulations, and the feasibility of our method is verified by experiments. Faced with issues involving a large measurement range, the proposed method is expected to give a competitive performance.

A Fringe Phase Extraction Method Based on Neural Network

In optical metrology, the output is usually in the form of a fringe pattern, from which a phase map can be generated and phase information can be converted into the desired parameters. This paper proposes an end-to-end method of fringe phase extraction based on the neural network. This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods. The results of simulation and experimental fringe patterns verify the accuracy and the robustness of this method. While it yields the same accuracy, the proposed method features easier operation and a simpler principle than the traditional phase-shifting method and has a faster speed than wavelet transform method.

Online fringe pitch selection for defocusing a binary square pattern projection phase-shifting method

A three-dimensional (3D) shape measurement system using defocusing binary fringe projection can perform high-speed and flexible measurements. In this technology, determining the fringe pitch that matches the current projection's defocus amount is of great significance for an accurate measurement. In this paper, we propose an online binary fringe pitch selection framework. First, by analyzing the fringe images captured by the camera, the defocus amount of projection can be obtained. Next, based on analysis of the harmonic error and camera noise, we establish a mathematical model of the normalized phase error. The fringe pitch that minimizes this normalized phase error is then selected as the optimal fringe pitch for subsequent measurements, which can also lead to more accuracy and robust measurement results. Compared with current methods, our method does not require offline defocus-distance calibration. However, it can achieve the same effect as the offline calibration method. It is also more flexible and efficient. Our experiments validate the effectiveness and practicability of the proposed method.

3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed

Structured light 3D shape metrology has become a very important technique and one of the hot research topics in 3D face recognition. However, it is still very challenging to use the digital light projector (DLP) in a 3D scanner and achieve high-speed, low-cost, small-size, and infrared-illuminated measurements. Instead of using a DLP, this paper proposes to use a galvanometer scanner to project phase-shifted fringes with a projection speed of infrared fringes up to 500 fps. Moreover, the measurement accuracy of multi-frequency (hierarchical) and multi-wavelength (heterodyne) temporal phase unwrapping approaches implemented in this system is analyzed. The measurement accuracy of the two methods is better than 0.2 mm. Comparisons are made between this method and the classical DLP approach. This method can achieve a similar accuracy and repeatability compared to the classical DLP method when a face mask is measured. The experiments on real human face indicate that this proposed method can improve the field of 3D scanning applications at a lower cost.