Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition.

The rising global population, in conjunction with the increasing demand, decreasing labor supply, and increasing costs in the agricultural sector, has induced a need for automation in this industry. Many of these tasks are simplified using depth images and are accomplished using the help of 3D sensing technology such as stereo vision and time of flight methods. While there are various merits to these approaches, there is a need for high-speed, high-accuracy 3D profiling approaches in this rapidly advancing industry. Fringe Projection Profilometry is a variation of structured light technology, which has the advantage of having high speed in the kilohertz range, and sub-millimeter accuracy, which could be extremely beneficial for this sector to adopt. In this article, we seek to provide a tutorial on this technology, explain its various principles along with the basic methodology, and expound on its advantages. We demonstrate some example results using soybean roots and spinach leaves to show its utility, discuss potential reasons as to why this has not yet been widely adopted by this industry, review its potential limitations, and examine possible ways those limitations can be addressed so that they do not present a roadblock in its adoption.

Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types.

Restoring overexposed fringe images in fringe projection profilometry (FPP) is a challenging task due to severe saturation and loss of fine structural details, which often renders traditional methods ineffective. In this work, we propose FDCGAN, a Frequency Domain Constrained Generative Adversarial Network designed to address this issue by combining spatial and frequency-domain learning. The proposed framework embodies a structurally coherent design that couples hierarchical feature encoding with multi-scale adversarial supervision, enabling both global structure preservation and local detail recovery, while normalization strategies regulate information flow and enhance resilience to photometric degradation. To further enhance restoration quality, we introduce a set of frequency-aware learning losses, including Fourier magnitude loss and high-frequency loss, which guide the model in reconstructing realistic fringe patterns. Experimental results demonstrate that FDCGAN significantly outperforms existing methods, especially under extreme overexposure, where fringe patterns and object shapes are barely visible. Even in such cases, FDCGAN can infer plausible fringe structures that significantly enhance the quality of 3D reconstruction. These findings highlight FDCGAN’s robustness and its potential for improving measurement reliability in real-world FPP systems under challenging lighting conditions.