Real-time 3D shape measurement

Since a slight variance in production processes can make the entire production run defective, defect inspections are indispensable procedures in manufacturing processes to ensure high quality of each item before entering the next manufacturing step. Three-dimensional (3D) optical shape measurement technologies are widely applied for surface defect inspection of complex workpieces because of its high-accuracy and digitization. However, the complex surface structure and position of the test object can pose serious challenges, making inspections still relatively slow, expensive, and complicated in implementation and maintenance. In this work, we propose a real-time 360∘ 3D surface defect inspection approach based on fringe projection profilometry without any auxiliary equipment for position control. Firstly, a multi-view 3D measurement based on geometric constraints is employed to acquire high-accuracy depth information from different perspectives. Then, a cycle-positioning-based registration scheme with the establishment of the pose-information-matched 3D standard digital model is proposed to realize rapid alignment of the measured point cloud and the standard model. Finally, a minimum 3D distance search is driven by a dual-thread processing mechanism for simultaneous scanning and detecting to quantify and locate 3D surface defects in real time. Experimental results show that our method can accurately identify the surface defects of complicated objects in real time in an extremely simple (hand-held) manner, saving a lot of operational expenses on precision alignment and position-orientation adjustment. The proposed method holds tremendous potential for quality control in many facets of industry, such as product development, testing, and manufacturing.

Since a slight variance in production processes can make the entire production run defective, defect inspections are indispensable procedures in manufacturing processes to ensure high quality of each item before entering the next manufacturing step. Three-dimensional (3D) optical shape measurement technologies are widely applied for surface defect inspection of complex workpieces because of its high-accuracy and digitization. However, the complex surface structure and position of the test object can pose serious challenges, making inspections still relatively slow, expensive, and complicated in implementation and maintenance. In this work, we propose a real-time 360∘ 3D surface defect inspection approach based on fringe projection profilometry without any auxiliary equipment for position control. Firstly, a multi-view 3D measurement based on geometric constraints is employed to acquire high-accuracy depth information from different perspectives. Then, a cycle-positioning-based registration scheme with the establishment of the pose-information-matched 3D standard digital model is proposed to realize rapid alignment of the measured point cloud and the standard model. Finally, a minimum 3D distance search is driven by a dual-thread processing mechanism for simultaneous scanning and detecting to quantify and locate 3D surface defects in real time. Experimental results show that our method can accurately identify the surface defects of complicated objects in real time in an extremely simple (hand-held) manner, saving a lot of operational expenses on precision alignment and position-orientation adjustment. The proposed method holds tremendous potential for quality control in many facets of industry, such as product development, testing, and manufacturing.

Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through “learning” on extensive data sets, the properly trained neural network can “predict” the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.

The digitization of the complete shape of real objects has essential applications in fields of intelligent manufacturing, industrial detection, and reverse modeling. In order to build the full geometric models of rigid objects, the object must be moved relative to the measurement system (or the scanner must be moved relative to the object) to obtain and integrate views of the object from all sides, which not only complicates the system configuration but makes the whole process time-consuming. In this Letter, we present a high-resolution real-time 360° three-dimensional (3D) model reconstruction method that allows one to rotate an object manually and see a continuously updated 3D model during the scanning process. A multi-view fringe projection profilometry system acquires high-precision depth information about a handheld object from different perspectives and, meanwhile, the multiple views are aligned and merged together in real time. Our system employs stereo phase unwrapping and an adaptive depth constraint that can unwrap the phase of dense fringe images robustly without increasing the number of captured patterns. We then develop an efficient coarse-to-fine registration strategy to match the 3D surface segments rapidly. Our experiments demonstrate that our method can reconstruct the high-precision complete 3D model of complex objects under arbitrary rotation without any instrument assist and expensive pre/post-processing.