Three-dimensional (3D) positioning with the correction of imaging aberrations in the photonic platform remains challenging. Here, we combine techniques from nanophotonics and machine vision to significantly improve the imaging and positioning performance. We use a titanium dioxide metalens array operating in the visible region to realize multipole imaging and introduce a cross-correlation-based gradient descent algorithm to analyze the intensity distribution in the image plane. This corrects the monochromatic aberrations to improve the imaging quality. Analysis of the two-dimensional aberration-corrected information in the image plane enables the 3D coordinates of the object to be determined with a measured relative accuracy of 0.60%–1.31%. We also demonstrate the effectiveness of the metalens array for arbitrary incident polarization states. Our approach is single-shot, compact, aberration-corrected, polarization-insensitive, and paves the way for future integrated photonic robotic vision systems and intelligent sensing platforms that are feasible on the submillimeter scale, such as face recognition, autonomous vehicles, microrobots, and wearable intelligent devices.

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Polarization plays a key role in both optics and photonics. Generally, the polarization states of light are measured with birefringent or dichroic optical elements paired with a power meter. Here we propose a direct polarization detection method based on colorimetric asymmetrical all-dielectric metasurfaces to obtain the polarization angles of the incident light. The independently tunable periods and diameters along the x and y axes enables double-layer nanopillars to realize high-performance dual-color palettes with arbitrary combinations under orthogonal polarization states. The polarization detection network based on residual networks is used to deeply learn the regulations between color palette variations and incident polarization angles, which can accurately recognize extremely slight polarization variations in about 1 s with an accuracy of 81.4% within 0.7° error and 99.5% within 1.4° error. Our strategy significantly improves the compactness of polarization detection, and it can be readily expanded to polarization distribution measurement and colorimetric polarization imaging on an intelligent platform.

Multicolor holography, which can store and reconstruct wavefront information of optical waves at multiple wavelength channels, is demonstrated as a powerful platform for colorful image display. Recently, interleaved and segmented metasurfaces have emerged as appealing alternatives to realize the multicolor holography. However, the crosstalk among different wavelength channels can severely lower their performance. How to obtain the nanostructures with on‐demand resonance wavelength, bandwidth, and phase delay is the key to overcome this challenge. Here, a hybrid framework composed of a neural network and an evolutionary strategy is proposed to implement the inverse design of nanostructures with desired resonance wavelength, bandwidth, and phase delay. With the proposed hybrid framework, the crosstalk between different wavelength channels can be eliminated by precisely controlling the resonance wavelength and the bandwidth of every nanostructure. As a proof of concept, a multicolor meta‐holography for linear polarized light is experimentally and theoretically validated. The proposed hybrid framework provides a powerful platform for the design of metasurfaces for multi‐frequency optical manipulation and multiplexing.

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