Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation.

Some cancers are related to atherosclerotic diseases; therefore, these two types of disease may share some antibody biomarkers in common. To investigate this, a first screening of sera was performed from patients with esophageal squamous cell carcinoma (ESCC) or acute ischemic stroke (AIS) for serological identification of antigens using recombinant cDNA expression cloning (SEREX). The amplified luminescent proximity homogeneous assay‐linked immunosorbent assay (AlphaLISA) method, which incorporates glutathione donor beads and anti‐human IgG acceptor beads, was used to evaluate serum antibody levels. SEREX screening identified low‐density lipoprotein receptor–related protein–associated protein 1 (LRPAP1) as a target antigen of serum IgG antibodies in the sera of patients with ESCC or AIS. Antigens, including recombinant glutathione S‐transferase–fused LRPAP1 protein, were prepared to examine serum antibody levels. AlphaLISA revealed significantly higher antibody levels against the LRPAP1 protein in patients with solid cancers such as ESCC and colorectal carcinoma and some atherosclerosis‐related diseases such as AIS and diabetes mellitus compared with healthy donors. Correlation analysis revealed that the elevated serum antibody levels against LRPAP1 were associated with smoking, a well‐known risk factor for both cancer and atherosclerosis. Serum LRPAP1 antibody is therefore a common marker for the early diagnosis of some cancers and atherosclerotic diseases and may reflect diseases caused by habitual smoking.

In fringe projection profilometry 3D measurement systems, the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras. The main solution involves using multiple exposures to modulate fringe intensity; however, it is inefficient. In this study, we introduce an attention-guided end-to-end phase calculation network to accelerate the multi-exposure structured light process for high dynamic range (HDR) measurements. We use attention modules to guide feature selection, enhancing relevant features and suppressing irrelevant features. Using the 12-step phase-shifting profilometry (PSP) as ground truth, our method accurately extracts the sine and cosine components of the fundamental frequency from a single pattern to retrieve the absolute phases. Tested on our metallic dataset requiring HDR imaging, our method achieves an absolute phase error of 0.084, close to that of the six-step PSP method (0.069), while using only 16.7% of the time. On the ceramic dataset, our method achieves 0.021 phase error, close to that of the four-step PSP (0.012). In quantitative measurements, our method achieves an accuracy of approximately 40 $$\upmu\text{m}$$ μ m on standard spheres and plates. Overall, our method preserves the accuracy of multi-exposure PSP methods while significantly accelerating the 3D reconstruction process.

In fringe projection profilometry systems, accurately reconstructing 3D objects with varying surface reflectivity requires high dynamic range (HDR) imaging. However, the limited dynamic range of single-exposure cameras poses challenges for capturing HDR fringe patterns efficiently. This paper introduces a deep learning-based HDR structured light 3D reconstruction pipeline, comprising an HDR Fringe Generation Module and a Phase Calculation Module. The HDR Fringe Generation Module employs an end-to-end network with attention guidance and feature distillation to reconstruct HDR fringe images from short-and long-exposure low dynamic range (LDR) inputs. The Phase Calculation Module processes the phase information from HDR fringes to enable 3D reconstruction. On a metallic HDR dataset, the method achieved a phase error of 0.105, comparable to the 4-exposure 6-step Phase Shifting Profilometry (PSP) method (0.069), with only 8.3% of the projection time. Experimental results demonstrate the robustness of our approach under diverse object geometries, exposure levels, and challenging global illumination environments. In quantitative measurements, our method achieved accuracies of sub-50 μm on ceramic spheres, flat plates and metal step object. Ablation experiments confirmed that feature distillation and attention module effectively enhance the HDR Fringe Generation Module, producing high-quality HDR fringe patterns critical for reconstructing objects with HDR surface reflectivity. Furthermore, we constructed an HDR imaging metal dataset comprising 1,700 samples of machined metal parts with diverse shapes, sizes, and materials, making it a benchmark in the field of HDR structured light measurement. Our method offers a general HDR imaging-based structured light 3D reconstruction approach, integrating the two modules into an efficient, end-to-end solution for objects with HDR reflective surfaces.

Precise and rapid three-dimensional (3D) measurement of metal parts on production lines is essential. Modern artificial intelligence (AI) supports sensing devices to see the world in 3D vision. Digital fringe 3D profilometry reconstruction (DF-3D) based on deep learning has the potential to revolutionize society. Nevertheless, attaining the high precision and robust DF-3D through a single-shot end-to-end (SS-E2E) AI network remains an unresolved challenge. For fast and precise 3D reconstruction, this work proposes an SS-E2E absolute phase prediction network strategy, composed of a multi-path branch auxiliary supervision network and guided by the fringe-phase physical model (MPS_XNet). Specifically, MPS_XNet is based on the three key stages of the phase retrieval model, including numerator, denominator, and wrapped phase supervision, as auxiliary branches to assist the main path of absolute phase for long-distance computation in interpretable features. Besides, we designed a physics-based supervisory loss function to correctly guide the network training along the path defined by the physical model. Experiments have demonstrated that MPS_XNet outperforms the state-of-the-art (SOTA) regression network paradigm on five datasets (metal workpieces and complex contours) and our measurement system, which not only compensates for insufficient input information but also robustly and interpretably guides the network training through converting the long-distance challenge to multiple short-distance sub-tasks. Our strategy achieves a groundbreaking 90% MAE reduction in phase retrieval through physical model design and task decomposition in 20 ms. It remains promising for future SOTA networks, enabling real-time, high-precision 3D measurement in industrial and scientific applications. The source code is publicly available at https://github.com/LiYiMingM/Physical-Model-Supervised-Machine-Learning-3D-reconstruction.