{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-etendue-expander-for-ultra-wide-angle","title":"Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display","arxiv_id":"2109.08123","date":"2021-09-16","proceeding":null,"authors":["Ethan Tseng","Grace Kuo","Seung-Hwan Baek","Nathan Matsuda","Andrew Maimone","Florian Schiffers","PRANEETH CHAKRAVARTHULA","Qiang Fu","Wolfgang Heidrich","Douglas Lanman","Felix Heide"],"abstract":"Holographic displays can generate light fields by dynamically modulating the wavefront of a coherent beam of light using a spatial light modulator, promising rich virtual and augmented reality applications. However, the limited spatial resolution of existing dynamic spatial light modulators imposes a tight bound on the diffraction angle. As a result, modern holographic displays possess low \\'{e}tendue, which is the product of the display area and the maximum solid angle of diffracted light. The low \\'{e}tendue forces a sacrifice of either the field-of-view (FOV) or the display size. In this work, we lift this limitation by presenting neural \\'{e}tendue expanders. This new breed of optical elements, which is learned from a natural image dataset, enables higher diffraction angles for ultra-wide FOV while maintaining both a compact form factor and the fidelity of displayed contents to human viewers. With neural \\'{e}tendue expanders, we experimentally achieve 64$\\times$ \\'{e}tendue expansion of natural images in full color, expanding the FOV by an order of magnitude horizontally and vertically, with high-fidelity reconstruction quality (measured in PSNR) over 29 dB on retinal-resolution images.","url_abs":"https://arxiv.org/abs/2109.08123v4","url_pdf":"https://arxiv.org/pdf/2109.08123v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-etendue-expander-for-ultra-wide-angle","repo_url":"https://github.com/nseungwoo/depolarized-holography","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}