Hybrid-quality-guided phase fusion model for high dynamic range 3D surface measurement by structured light technology

Parameter selection on a multi-exposure fusion method for measuring surfaces with varying reflectivity in microscope fringe projection profilometry

As industrial and scientific advancements continue, the demand for precise measurement of three-dimensional (3D) shapes and surfaces is steadily increasing. However, accurate 3D measurement of certain surfaces, especially those with varying reflectivities, has always been a challenging issue. Multi-exposure fusion methods have shown stable, high-quality measurement results, but the selection of parameters for these methods has largely been based on experience. To address this issue, this paper has improved the multi-exposure fusion method and introduced a guided approach for parameter selection, significantly enhancing the completeness of measurement results. Additionally, a comparative model is developed to experimentally validate the specific impacts of Gaussian window variance, optimal grayscale range, and attenuation factor variance on the integrity of 3D reconstruction. The experimental results demonstrate that under the guidance of the parameter adjustment method proposed in this paper, the multi-exposure fusion for measuring the 3D topography of high-dynamic surfaces improves the restoration coverage from the original 86% (bright areas) and 50% (dark areas) to over 99%. This provides a selection strategy for parameter adjustment guidance in precise measurements based on the multi-exposure method.

Physics-based supervised learning method for high dynamic range 3D measurement with high fidelity

High dynamic range (HDR) 3D measurement is a meaningful but challenging problem. Recently, many deep-learning-based methods have been proposed for the HDR problem. However, due to learning redundant fringe intensity information, their networks are difficult to converge for data with complex surface reflectivity and various illumination conditions, resulting in non-robust performance. To address this problem, we propose a physics-based supervised learning method. By introducing the physical model for phase retrieval, we design a novel, to the best of our knowledge, sinusoidal-component-to-sinusoidal-component mapping paradigm. Consequently, the scale difference of fringe intensity in various illumination scenarios can be eliminated. Compared with conventional supervised-learning methods, our method can greatly promote the convergence of the network and the generalization ability, while compared with the recently proposed unsupervised-learning method, our method can recover complex surfaces with much more details. To better evaluate our method, we specially design the experiment by training the network merely using the metal objects and testing the performance using different diffuse sculptures, metal surfaces, and their hybrid scenes. Experiments for all the testing scenarios have high-quality phase recovery with an STD error of about 0.03 rad, which reveals the superior generalization ability for complex reflectivity and various illumination conditions. Furthermore, the zoom-in 3D plots of the sculpture verify its fidelity on recovering fine details.

Adaptive phase retrieval algorithm for local highlight area based on a piecewise sine function

Phase measuring profilometry (PMP) has been widely used in industries for three-dimensional (3D) shape measurement. However, phase information is often lost due to image saturation results from high-reflection object surfaces, leading to subsequent 3D reconstruction errors. To address the problem, we propose an adaptive phase retrieval algorithm that can accurately fit the sinusoidal fringes damaged by high reflection in the saturated regions to retrieve the lost phase information. Under the proposal, saturated regions are first identified through a minimum error thresholding technique to narrow down regions of interest and so that computation costs are reduced. Then, images with differing exposures are fused to locate peak-valley coordinates of the fitting sinusoidal fringes. And the corresponding values of peak-valley pixels are obtained based on a least squares method. Finally, an adaptive piecewise sine function is constructed to recover the sinusoidal fringe pattern by fitting the pattern intensity distribution. And the existing PMP technology is used to obtain phase information from the retrieved sinusoidal fringes. To apply the developed method, only one (or two) image with different exposure times is needed. Compared with existing methods for measuring reflective objects, the proposed method has the advantages of short operation time, reduced system complexity, and low demand on hardware equipment. The effectiveness of the proposed method is verified through two experiments. The developed methodology provides industry an alternative way to measure high-reflection objects in a wide range of applications.

Enhanced Fourier-Hilbert-transform suppression for saturation-induced phase error in phase-shifting profilometry

Intensity saturation tends to induce severe errors in high dynamic range three-dimensional measurements using structured-light techniques. This paper presents an enhanced Fourier-Hilbert-transform (EFHT) method to suppress the saturation-induced phase error in phase-shifting profilometry, by considering three types of residual errors: nonuniform-reflectivity error, phase-shift error, and fringe-edge error. Background normalization is first applied to the saturated fringe patterns to suppress the effect of the nonuniform reflectivity. A self-correction method is proposed to correct the large phase-shift error in the compensated phase. The self-corrected phase error is detected to assist in locating the fringe-edge area, within which the true phase is computed based on the sub-period phase error model. Experimental results demonstrated the effectiveness of the proposed method in suppressing the saturation-induced phase error and other three types of residual errors with fewer images.

Application of three-dimensional reconstruction technology in dentistry: a narrative review

BACKGROUND

Three-dimensional(3D) reconstruction technology is a method of transforming real goals into mathematical models consistent with computer logic expressions and has been widely used in dentistry, but the lack of review and summary leads to confusion and misinterpretation of information. The purpose of this review is to provide the first comprehensive link and scientific analysis of 3D reconstruction technology and dentistry to bridge the information bias between these two disciplines.

METHODS

The IEEE Xplore and PubMed databases were used for rigorous searches based on specific inclusion and exclusion criteria, supplemented by Google Academic as a complementary tool to retrieve all literature up to February 2023. We conducted a narrative review focusing on the empirical findings of the application of 3D reconstruction technology to dentistry.

RESULTS

We classify the technologies applied to dentistry according to their principles and summarize the different characteristics of each category, as well as the different application scenarios determined by these characteristics of each technique. In addition, we indicate their development prospects and worthy research directions in the field of dentistry, from individual techniques to the overall discipline of 3D reconstruction technology, respectively.

CONCLUSIONS

Researchers and clinicians should make different decisions on the choice of 3D reconstruction technology based on different objectives. The main trend in the future development of 3D reconstruction technology is the joint application of technology.

High dynamic reflection surface 3D reconstruction with sharing phase demodulation mechanism and multi-indicators guided phase domain fusion

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.