Vision-based three-dimensional (3D) shape measurement techniques have been widely applied over the past decades in numerous applications due to their characteristics of high precision, high efficiency and non-contact. Recently, great advances in computing devices and artificial intelligence have facilitated the development of vision-based measurement technology. This paper mainly focuses on state-of-the-art vision-based methods that can perform 3D shape measurement with high precision and high resolution. Specifically, the basic principles and typical techniques of triangulation-based measurement methods as well as their advantages and limitations are elaborated, and the learning-based techniques used for 3D vision measurement are enumerated. Finally, the advances of, and the prospects for, further improvement of vision-based 3D shape measurement techniques are proposed.

Digital fringe projection (DFP) three-dimensional (3-D) shape measurement technique has been extensively researched and applied due to its advantages of high accuracy and high efficiency. To retain the measurement accuracy for large objects, results of multiple measurements are required to be aligned based on the combination of DFP and auxiliary positioning instruments, which decreases the measurement efficiency and limits the practical application. This article presents an accurate and efficient scheme for large-scale 3-D shape measurement based on speckle-assisted fringe projection and global markers localization. Taking advantage of the triple-view geometric constraints and stereo structured-light (SL) model, arbitrary 3-D surface can be reconstructed with high quality by projecting only four patterns. To align multiple local shape data of large objects without using auxiliary positioning instruments, an accurate data registration method based on global markers reconstruction and localization is presented, which first calculates the 3-D coordinates of all noncoded markers pasted on the object surface by sequential matching, incremental markers reconstruction, and global optimization based on bundle adjustment followed by multiple local data registration through local-to-global iterative closest point (ICP) matching. The experimental results indicate that the proposed method can substantially decrease the accumulated measurement error and achieve commensurate accuracy with the auxiliary positioning methods.

Multi-view fringe projection profilometry systems that integrate a telecentric projector with multiple oblique-view cameras offer unique advantages for inspecting high dynamic-range surfaces featuring densely packed, intricate microstructures. Nevertheless, such systems encounter fundamental calibration challenges, namely, sign ambiguity in the rotation matrices and truncated extrinsic parameters inherent to telecentric projector models, as well as difficulties in multi-view point cloud registration. This paper introduces a novel calibration framework with three principal contributions. First, we resolve the sign ambiguity by calibrating the telecentric projector under a quasi pinhole model and directly transferring the extrinsic sign conventions, thereby obviating the need for costly precision displacement stages or elaborate virtual targets. Second, we fix the axial-gauge freedom by constraining the origin of the projector coordinate system to lie on the XY-plane of the camera coordinate system. Third, we establish precise relative poses between all cameras and a designated reference camera, enabling unified multi-view point cloud registration directly within the projector coordinate frame, which substantially reduces alignment errors and accelerates data processing. Experimental results demonstrate marked improvements in accuracy: reprojection root-mean-square errors of 0.084 pixels for the cameras and 0.106 pixels for the projector, corresponding to in-plane spatial resolutions of 0.21 µm and 0.26 µm, respectively. The proposed method offers a robust solution for micron-level inspection in semiconductor packaging and precision manufacturing.