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360° structured light with learned metasurfaces


Structured light has proven instrumental in three-dimensional imaging, LiDAR and holographic light projection. Metasurfaces, comprising subwavelength-sized nanostructures, facilitate 180°-field-of-view structured light, circumventing the restricted field of view inherent in traditional optics like diffractive optical elements. However, extant-metasurface-facilitated structured light exhibits sub-optimal performance in downstream tasks, due to heuristic design patterns such as periodic dots that do not consider the objectives of the end application. Here we present 360° structured light, driven by learned metasurfaces. We propose a differentiable framework that encompasses a computationally efficient 180° wave propagation model and a task-specific reconstructor, and exploits both transmission and reflection channels of the metasurface. Leveraging a first-order optimizer within our differentiable framework, we optimize the metasurface design, thereby realizing 360° structured light. We have utilized 360° structured light for holographic light projection and three-dimensional imaging. Specifically, we demonstrate the first 360° light projection of complex patterns, enabled by our propagation model that can be computationally evaluated 50,000× faster than the Rayleigh–Sommerfeld propagation. For three-dimensional imaging, we improve the depth-estimation accuracy by 5.09× in root-mean-square error compared with heuristically designed structured light. Such 360° structured light promises robust 360° imaging and display for robotics, extended-reality systems and human–computer interactions.

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Fig. 1: 360° structured light enabled by a learned metasurface.
Fig. 2: 360° structured light with a learned metasurface.
Fig. 3: 3D imaging with 360° structured light.
Fig. 4: Experimental demonstration of 360° 3D imaging.

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Data availability

Our 360° synthetic dataset and the learned metasurface phase map are available via GitHub at

Code availability

The code used to generate the findings of this study is available via GitHub at


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S.-H.B. acknowledges the National Research Foundation (NRF) grants (RS-2023-00211658, NRF-2022R1A6A1A03052954) funded by the Ministry of Science and ICT (MSIT) and the Ministry of Education of the Korean government, and the Samsung Research Funding & Incubation Center for Future Technology grant (SRFC-IT1801-52) funded by Samsung Electronics. J.R. acknowledges the Samsung Research Funding & Incubation Center for Future Technology grant (SRFC-IT1901-52) funded by Samsung Electronics, the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCO, the NRF grants (RS-2024-00356928, RS-2024-00337012, RS-2024-00416272, NRF-2022M3C1A3081312) funded by the MSIT of the Korean government, and the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean government. G.K. acknowledges the NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government.

Author information

Authors and Affiliations



S.-H.B., J.R., E.C. and G.K. conceived the idea and initiated the project. E.C. designed the propagation model. E.C., G.K. and J.Y. verified the propagation model. E.C. and Y.J. performed the end-to-end training and synthetic experiments. E.C., G.K. and Y.J. implemented the experimental prototype. G.K. fabricated the devices. J.R. guided the material characterization and device fabrication. All authors participated in discussions and contributed to writing the paper. All authors confirmed the final paper. S.-H.B. and J.R. guided all aspects of the work.

Corresponding authors

Correspondence to Junsuk Rho or Seung-Hwan Baek.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Photonics thanks Haoran Ren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Additional experimental demonstration of 360° light projection.

ac 360° projection onto a hemispherical screen. df Bird-eye view of 360° projection in an enclosed room.

Extended Data Fig. 2 Qualitative comparison of depth estimation.

We captured an indoor scene with various objects for qualitative comparisons of 3D imaging. We compared our 360° structured light against 360° multi-dot structured light and passive stereo. The red boxes in the image represent a flat and smooth floor. 360° structured light successfully reconstructs the depth map of the scene, while the multi-dot structured light and passive method show noticeable holes and lack smoothness in their results. When compared to the heuristically-designed multi-dot structured light, our 360° structured light with learned metasurface yields more robust 3D imaging performance exploiting its non-uniform intensity distribution and distinct features on potential locations of corresponding point. Passive stereo struggles with texture-less scenes and it inevitably requires sufficient ambient illumination. Our 360° structured light enables robust 3D imaging under low ambient light conditions. Please refer Supplementary Note 13 for the method to ensure a fair comparison and additional evaluation.

Extended Data Fig. 3 Additional experiment results of 3D imaging with 360° structured light.

This presents additional qualitative results of various real-world scenes. It shows that 360° structured light enables accurate reconstruction on the four additional scenes containing various objects, including furniture, dolls, umbrellas, balls and human subjects.

Extended Data Fig. 4 Additional qualitative results on synthetic scenes.

The qualitative results are illustrated, which include rendered images and estimated depth for each comparative method. In the qualitative evaluation, our 360° structured light outperforms the other methods. Notably, in texture-less scenes, the performance gap is more pronounced.

Supplementary information

Supplementary Information

Supplementary Notes 1–15, Figs. 1–15 and Tables 1–9.

Supplementary Video 1

Our end-to-end learning process. The video consists of two parts. First, it demonstrates the learned metasurface phase map and the corresponding structured-light pattern. As discussed in the main text, the initial iterations of training establish the overall structure of the pattern, whereas subsequent iterations refine the finer details. This progression is visualized in the video. Next, the video showcases the simulation of a captured image, the estimated depth map and the ground truth during the training iterations. In the first stage of training, the captured image evolves as the metasurface phase map is learned in conjunction with the depth-estimation network. The video shows this dynamic process, highlighting the learned structured light. In the second stage of training, the metasurface phase map is fixed, and the focus solely shifts to optimizing the depth-estimation network. Consequently, the captured image remains consistent throughout this stage, whereas the quality of the estimated depth map progressively improves with the refined depth-estimation network.

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Choi, E., Kim, G., Yun, J. et al. 360° structured light with learned metasurfaces. Nat. Photon. (2024).

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