MEMS Laser beam scanning (LBS) has been identified as a key advancement for augmented reality (AR) displays due to its ability to create compact optical systems that generate bright, high-contrast images with minimal heat dissipation. This innovation can be attributed to the focus-free, efficient light-on-demand pixel projection mechanisms integral to LBS. The LBS, specifically in Lissajous-mode, outperforms the raster-mode in terms of larger scan angles and stability to external vibrations, by leveraging a MEMS mirror operating at bi-axial resonance. However, it tends to be hampered by small mirror aperture, low fill-factor, and inconsistent uniformity of image projection. In this research, a unique gimbal-less Lissajous MEMS scanner was proposed. It employs a bi-axial high frequency of 12,255 Hz and 7,182 Hz to achieve a resolution of 640 × 360 pixels and a video refresh rate of 57 Hz, all while maintaining a high image fill factor of 85.11%. The robust structure of the mirror is proven to sustain stable scanning under broad spectrum of external vibration disturbance up to 2,000 Hz. Furthermore, the large mirror diameter of 2 mm improves refined pixel projection and increased optical etendue for exit pupil. Mathematic model of Lissajous pixel-cells and image reconstruction simulation were established to validate the LBS's ability to generate a uniform and densely pixelated visual effect that fits for typical AR head-up display (AR-HUD). In a pioneering move, performance metric of figure-of-merit was defined to evaluate AR light-engines using varied picture-generation techniques, laying a foundation for guiding future AR system development.

Structured light 3D imaging systems commonly employ panel-based projectors or 1-axis MEMS mirrors with beam expander lens to project multi-frame barcodes or dot clouds, addressing challenges posed by objects with multi-scale feature sizes. However, these methods often result in large system volumes due to the required projection multi-lens modules, high hardware costs, or limited light pattern generation capabilities that hindering measurement precision enhancement. This paper introduces an innovative approach to reconfigurable spatial light pattern projection using a single bi-axial MEMS mirror with Lissajous scanning. In contrast to the pixel-by-pixel pre-defined image patterns encoding of conventional 2D laser beam scanning, the proposed method simply aligns the MEMS bi-axial resonance frequencies with laser pulse modulation, enabling the projection of diverse structured light patterns such as stripes, lines, dot matrices, and random dot clouds, which can adapt to different 3D imaging algorithms demands. It eliminates the need for multi-frame encoding and streamlines data caching, simplifies digital logic hardware. A prototype 3D imaging system was developed to demonstrate the mathematical model for laser modulation and the technical feasibility based on the proposed principle. Beyond its lens-free essence, the system supports focal-free optics and a compact projection form factor, which accommodates to a broad range of projection distances and field-of-views based on object’s location. 3D depth map of polynomial surface and blocks objects are extracted through single-frame pattern projection with a relative high accuracy. The presented modulation theory for diverse structured light pattern generation opens avenues for versatile and compact 3D imaging applications of LiDAR and robotic 3D vision.

Balancing speed and accuracy has always been a challenge in 3D reconstruction. One-shot structured light illuminations are of perfect performance on real-time scanning, while the related 3D point clouds are typically of relatively poor quality, especially in regions with rapid height changes. To solve this problem, we propose a one-shot reconstruction scheme based on shearlet transform, which combines spatial and frequency domain information to enhance reconstruction accuracy. First, we apply the shearlet transform to the deformed fringe pattern to obtain the transform coefficients. Second, pixel-wise select the indices associated with the N largest coefficients in magnitude to obtain a new filter. Finally, we refocus globally to extract phase using these filters and generate a reliable quality map based on coefficient magnitudes to guide phase unwrapping. Simultaneously, we utilize the maximum coefficient value to generate a quality map for guiding the phase unwrapping process. Experimental results show that the proposed method is robust in discontinuous regions, resulting in more accurate 3D point clouds.

In fringe projection profilometry, applying pre-distortion to fringe patterns reduces the errors caused by projector lens distortion. However, it is important to note that discontinuous fringe patterns, such as binary fringe patterns, introduce additional errors when using pre-distortion methods. While post-undistortion methods are applicable for discontinuous fringe patterns, the computation is typically time-consuming. We propose a linear-grid model for correcting lens distortion. First, we select multiple equidistant points within the grid to calculate the linear parameters and store them as look-up tables (LUTs). Second, by rounding down the captured distorted point to the nearest integer point, we obtain the index value for LUTs. Finally, we achieve real-time compensation for distortion error through linear expressions. The experimental results show that the proposed effectively mitigates the distortion by a factor of 6 × in terms of root mean squared error. Additionally, it exhibits a computational speed of 409.50 fps, which is an improvement compared to the traditional iterative model at 39.48 fps and the scale-offset model at 264.48 fps.

In structured light illumination, lens distortions in both the camera and the projector compromise the accuracy of 3D reconstruction. Typically, existing methods separately compensate for camera and projector lens distortion. In this paper, we report a novel joint distortion model that analytically relates distorted 3D coordinate to its undistorted counterpart, thereby directly recovering distortion-free 3D coordinate from distorted one. First, we conduct a typical 3D scanning to have the distorted 3D coordinate. Second, we derive a set of linear equations of undistorted coordinate, whose coefficient matrix is represented by the distorted 3D coordinate and calibration parameters. Finally, we straightforwardly compute the corrected 3D coordinate using the least square method. Extensive experiments show that, compared with the distorted point cloud, our method effectively reduces the lens distortion of the system by a factor of 5 in root mean squared error, outperforming the existing methods in terms of accuracy.

Structured light illumination is widely applied for surface defect detection due to its advantages in terms of speed, precision, and non-contact capabilities. However, the high reflectivity of metal surfaces often results in the loss of point clouds, thus reducing the measurement accuracy. In this paper, we propose a novel quaternary categorization strategy to address the high-reflectivity issue. Firstly, we classify the pixels into four types according to the phase map characteristics. Secondly, we apply tailored optimization and reconstruction strategies to each type of pixel. Finally, we fuse point clouds from multi-type pixels to accomplish precise measurements of high-reflectivity surfaces. Experimental results show that our strategy effectively reduces the high-reflectivity error when measuring metal surfaces and exhibits stronger robustness against noise compared to the conventional method.