High‐Resolution 3D Imaging with Tunable Point Cloud Projection Based on Meta‐Device
Yin Zhou, Zhengdong Chen, Jialuo Cheng, Qican Zhang, Zihan Geng, Zhoujie Wu, Mu Ku Chen
Abstract
Abstract 3D imaging is a crucial way to record stereoscopic information of real‐world objects in machine vision, medical plastic surgery, heritage conservation, and other applications for creating comprehensive topography information databases. The point cloud projection based on the metasurface has the characteristics of a large field of view and depth of field. However, once the metasurface is completed, the spatial sampling rate is fixed and challenging to improve, resulting in a low‐resolution reconstruction. In this study, a meta‐based tunable point cloud device composed of a meta‐lens array and a tunable objective collaborative optimization is proposed. The meta‐device can project tunable point clouds to change the sampling rate of objects' spatial information. The proposed meta‐device has experimentally demonstrated the features of high spatial resolution (17 times better than the original) with an accuracy of 0.035 mm. Such a design can achieve high spatial and depth resolution while keeping the system size small. This concept opens new possibilities for applications such as facial recognition, wearable devices, and human‐computer interactions. It opens up new progress in industrial and consumer 3D reconstruction.