Light detection and ranging (LiDAR) is widely used for active three-dimensional (3D) perception. Beam scanning LiDAR provides high accuracy and long detection range with limited detection efficiency, while flash LiDAR can achieve high-efficiency detection through the snapshot approach at the expense of reduced accuracy and range. With the synergy of these distinct detection approaches, we develop a miniaturized dual-mode, reconfigurable beam forming device by cascading Pancharatnam-Berry phase and propagation phase metasurfaces, integrated with a micro-actuator. By modulating incident light polarization, we can switch the output beam of the device between the beam array scanning mode and flash illuminating mode. In the scanning mode, the device demonstrates a continuously tunable angular resolution and a ± 35° field of view (FoV) through driving the micro-actuator to achieve the lateral translation of ±100 μm. In the flash mode, uniform illumination across the entire FoV is achieved. As a proof of concept, we propose an adaptive 3D reconstruction scheme that leverages the device’s capability to switch operation modes and adjust detection resolution. Together, the proposed device and the detection scheme constitute a dual-mode LiDAR system, demonstrating high adaptability to diverse environments and catalyze the applications of more efficient and compact 3D detection systems.

Real-time localization and classification of fast-moving objects are crucial in various applications. Traditional imaging approaches face significant challenges, including large data requirements, limited update rates, motion blur, and restrictions in non-visible wavelengths. This paper proposes an image-free method based on complementary single-pixel detection and centralized geometric moments, which effectively integrates target localization and classification into a unified framework. By employing only four specific illumination patterns, the method can simultaneously determine the centroid position and shape of the target at an update rate of up to 5.55 kHz. Theoretical simulations verify the robustness of the proposed method under similarity transformations. Experimental results indicate that the proposed system achieves accurate real-time target localization and classification under diverse conditions, with an RMSE for centroid localization below 0.5 pixels and 93.3% classification accuracy for 30 different objects. The proposed method demonstrates strong adaptability to complicated environments. It holds significant potential for applications in target tracking, character recognition, industrial automation, and the development of optoelectronic neural networks for advanced optical computing tasks.

This paper reviews the field of cascaded metasurfaces, which are advanced optical devices formed by stacking or serially arranging multiple metasurface layers. These structures leverage near-field and far-field electromagnetic (EM) coupling mechanisms to enhance functionalities beyond single-layer metasurfaces. This review comprehensively discusses the physical principles, design methodologies, and applications of cascaded metasurfaces, focusing on both static and dynamic configurations. Near-field-coupled structures create new resonant modes through strong EM interactions, allowing for efficient control of light properties like phase, polarization, and wave propagation. Far-field coupling, achieved through greater interlayer spacing, enables traditional optical methods for design, expanding applications to aberration correction, spectrometers, and retroreflectors. Dynamic configurations include tunable devices that adjust their optical characteristics through mechanical motion, making them valuable for applications in beam steering, varifocal lenses, and holography. This paper concludes with insights into the potential of cascaded metasurfaces to create multifunctional, compact optical systems, setting the stage for future innovations in miniaturized and integrated optical devices.

Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes a single-pixel detection system for adaptive multi-target tracking based on the geometric moment and the exponentially weighted moving average (EWMA). The proposed system leverages geometric moments for high-speed target localization, requiring merely $\mathit{3}\mathit{N}$ measurements to resolve centroids for $\mathit{N}$ targets. Furthermore, the output values of the system are used to continuously update the weight parameters, enabling adaptation to varying motion patterns and ensuring consistent tracking stability. Experimental validation using a digital micromirror device (DMD) operating at 17.857 kHz demonstrates a theoretical tracking update rate of 1984 Hz for three objects. Quantitative evaluations under 1920 × 1080 pixel resolution reveal a normalized root mean square error (NRMSE) of 0.00785, confirming the method’s capability for robust multi-target tracking in practical applications.

This paper presents a metasurface‐based light receiver tailored for compact off‐axis light detection and ranging (LiDAR) systems, addressing the critical challenge of simultaneously enhancing the field of view (FOV) and effective signal reception while adhering to strict size and weight limitations. A general design principle for the metasurface‐based light receiver with large FOV capability is proposed, leveraging mapping relations to achieve optimal performance. As a proof of concept, a 20‐mm‐diameter 4‐region metasurface device was designed and fabricated by deep ultraviolet (DUV) projection stepper lithography on an 8‐inch fused silica wafer. The metasurface‐based light receiver achieves a large FOV of ±30° and demonstrates a significant power enhancement ranging from 1.5 to 3 times at 940 nm when coupled with a 3‐mm‐diameter avalanche photodiode (APD). The innovation not only establishes a new paradigm for compact, high‐performance LiDAR systems but also enables deployment in advanced fields such as unmanned aerial vehicles (UAVs) and miniaturized robots.

Rapid and reliable target classification is essential in resource-constrained vision applications, yet conventional imaging-based approaches suffer from severe performance degradation under optical aberrations, defocus, and motion blur. Single-pixel detection has recently emerged as a promising solution for low-cost and high-speed sensing, but existing methods mainly address motion-induced degradation and still rely on image reconstruction or computationally intensive recognition pipelines. In this Letter, we present a fast and blur-resilient classification framework that directly extracts blur-invariant features from single-pixel measurements using only seven fixed DMD modulation masks. The proposed system bypasses the traditional “image-then-recognize” paradigm and instead enables a direct “measure-to-recognize” workflow without the need for image processing or neural network training. Extensive simulations and experiments demonstrate 98.96% recognition accuracy on the test dataset at an update rate of 2.551 kHz under various degraded imaging conditions. The proposed framework provides a simple and efficient solution for blur-robust recognition and opens a new, to the best of our knowledge, avenue for high-speed optical intelligence in challenging imaging environments.