The binary encoding fringe projection profilometry has the potential of achieving high-speed 3-D measurement. Error diffusion is one of the most widely investigated binary encoding techniques. However, the existing error diffusion usually utilizes the same diffusion kernel to encode sinusoidal (S) fringe patterns without considering the fringe pitch-widths, leading to inevitable measurement error due to the demodulated phase error. In this article, a fringe binary encoding strategy based on genetic algorithm is proposed to reduce the phase error recovered by the temporal phase unwrapping (TPU) method. We design an optimization equation that combines phase error and intensity error to calculate the encoding error, which ensures that the optimized diffusion kernel is robust to defocusing changes. Both simulation and experiments have proved the effectiveness and accurateness of the proposed method. Comparative experiments indicate that the extracted phase error is greatly reduced for two types of extensively adopted TPU methods at different defocusing levels. Especially for multi wavelength method, the phase error can be reduced by up to 68%.

Accurate and complete 3D measurement of complex high dynamic range (HDR) surfaces has been challenging for structured light projection technique. The behavior of spraying a layer of diffuse reflection material, which will inevitably incur additional thickness. Existing methods based on additional facilities will increase the cost of hardware system. The algorithms-based methods are cost-effective and nondestructive, but they generally require redundant patterns for image fusion and model training, which fail to be suitable for practicing automated 3D measurement for complex HDR surfaces. In this paper, a HDR surface 3D reconstruction method based on sharing demodulation phase unwrapping mechanism and multi-indicators guided phase fusion strategy is proposed. The division of the exposure interval is optimized via the image entropy to generate an optimal exposure sequence. The combination of temporal-spatial binary (TSB) encoding fringe patterns with time-integration strategy and the variable exposure mode of digital mirror device (DMD)-based projector with a minimum projection exposure time of 233μs enables the proposed approach to broadly adapt complex HDR surfaces. We propose an efficient phase analysis solution called sharing mechanism that wrapped phase sequences from captured different intensity fringe images are unwrapped through sharing the same group of misaligned Gray code (MGC) decoding result. Finally, a phase sequences fusion model guided by multi-indicators, including exposure quality, phase gradient smoothness and pixel effectiveness, is established to obtain an optimum phase map for final 3D reconstruction. Comparative experiments indicate that the proposed method can completely restore the 3D topography of HDR surfaces with the images reduction of at least 65% and the measurement integrity is maintained at over 98% while preserving the measurement accuracy and excluding the outliers.

Deep learning (DL)-based single-frame fringe pattern to 3D depth reconstruction methods have aroused extensive research interest. The goal is to estimate high-precision 3D shape from a single frame of fringe pattern with limited information. Therefore, the purpose of this work attempts to propose an end-to-end DL-based 3D reconstruction method from the single fringe pattern with excellent capability of achieving high accuracy depth recovery and geometry details preservation of tested objects. We construct a multi-scale feature fusion convolutional neural network (CNN) called MSUNet++, which incorporates discrete wavelet transform (DWT) in data preprocessing for extracting high-frequency signals of fringe patterns as input of the network. Additionally, a loss function that combines structural similarity with edge perception is established. Through these measures, high-frequency geometry details of the reconstruction results can be obviously enhanced, while the geometric shape can be effectively maintained. Ablation experiments are involved in validating the effectiveness of our proposed solution. 3D reconstructed results and analysis of generalization experiments on different tested samples imply that the proposed method in this research enjoys capabilities of higher accuracy, better detail preservation, and robustness in comparison with the compared methods.

Speckle projection profilometry (SPP) determines the global correspondence between stereo images by speckle pattern(s) projection in three-dimensional (3D) vision. However, it is extremely challenging for traditional algorithms to achieve a satisfactory 3D reconstruction accuracy generally via single-frame speckle pattern, which heavily constraints the application in dynamic 3D imaging. Recently some deep learning (DL) based methods have made process in this issue but there exist deficiencies in feature extraction, leading to a limited improvement in accuracy. In this paper, we propose a stereo matching network called Densely Connected Stereo Matching (DCSM) Network that requires only single-frame speckle pattern as input, adopts densely connected feature extraction and incorporates attention weight volume construction. The densely connected multi-scale feature extraction module we constructed in DCSM Network has a positive effect on the combination of global and local information and inhibition of information loss. We also establish a real measurement system and its digital twin through Blender to obtain rich speckle data under SPP framework. Meanwhile, we introduce Fringe Projection Profilometry (FPP) to obtain phase information to assist in generating high-precision disparity as Ground Truth (GT). Experiments with different types of models and models with various perspectives are implemented to prove the effectiveness and generalization of the proposed network compared with classic and the latest DL-based algorithms. Finally, the 0.5-Pixel-Error of our method in the disparity maps is as low as 4.81%, and the accuracy is verified to be improved by up to 33.4%. As for the cloud point, our method has a reduction of 18%∼30% compared with other network-based methods.

The binary defocusing fringe projection profilometry (FPP) technique has demonstrated various advantages for high-speed and high-accuracy three-dimensional (3D) surface measurement. However, higher fringe frequency does not necessarily give better measurements in binary defocusing FPP. To improve the 3D geometry measurement accuracy, this paper proposes an optimal frequency selection approach by analyzing the phase error distribution under different defocusing degrees. The phase error is analyzed theoretically based on the multi-frequency temporal phase unwrapping process, and the associated relationship with fringe frequency, system defocusing degree, noise, and other influencing factors is established. Meanwhile, optimal fringe frequency in a specific system is selected by the theoretical model combined with the validation of simulation experiments. Finally, the measurement accuracy could be effectively enhanced by the generated binary fringe patterns of optimal frequency. Both simulations and experiments verify the effectiveness and robustness of the proposed method.

Digital fringe projection (DFP) with defocused binary fringe patterns has the ability to overcome the projector nonlinearity and achieve a high-speed 3D measurement. The Floyd–Steinberg (FS) dithering technique is one of the most commonly adopted binary fringe coding methods due to its relatively high measurement accuracy. Nevertheless, we found that the FS binary fringe would cause a fixed error in the recovered phase, which is proven to be invariable for various defocusing levels and various phase-shift steps according to the analysis of the phase error based on noise model of phase-shifting profilometry. It means that FS binary fringe would have a certain offset in space, compared with standard sinusoidal fringe, which is verified to be essentially constant for different fringe pitches through simulation and experiment. This offset would distort the 3D geometry of the tested target for monocular systems relying on triangulation, which needs to be compensated to improve 3D measurement accuracy. Experiments are presented to demonstrate the enhanced 3D result after compensation.