Metasurface-enabled 3D imaging via local bright spot gray scale matching using the structured light dot array

Three-dimensional (3D) imaging is widely utilized in various applications, such as light detection, autonomous vehicles, and machine vision. However, conventional 3D imaging systems often rely on bulky optical components. Metasurfaces, as next-generation optical devices, possess flexible wavefront modulation capabilities and excellent combination with computer vision algorithms. Here, we propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface, covering a 2π-FOV, for projecting coded point clouds in Fourier space. We explore a local bright spot gray scale matching algorithm for depth extraction, enabling 3D imaging. This algorithm simplifies the data processing flow and optimizes depth extraction and feature matching processes through a customized region gray scale comparison. As a result, it effectively reduces computational complexity and enhances tolerance to image quality fluctuations. The proposed approach provides new possibilities for developing compact and high-performance planar 3D optical imaging devices, which will drive the advancement of fields such as computer vision and artificial intelligence.