{"ID":2861751,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03356","arxiv_id":"2510.03356","title":"Learned Display Radiance Fields with Lensless Cameras","abstract":"Calibrating displays is a basic and regular task that content creators must perform to maintain optimal visual experience, yet it remains a troublesome issue. Measuring display characteristics from different viewpoints often requires specialized equipment and a dark room, making it inaccessible to most users. To avoid specialized hardware requirements in display calibrations, our work co-designs a lensless camera and an Implicit Neural Representation based algorithm for capturing display characteristics from various viewpoints. More specifically, our pipeline enables efficient reconstruction of light fields emitted from a display from a viewing cone of 46.6° X 37.6°. Our emerging pipeline paves the initial steps towards effortless display calibration and characterization.","short_abstract":"Calibrating displays is a basic and regular task that content creators must perform to maintain optimal visual experience, yet it remains a troublesome issue. Measuring display characteristics from different viewpoints often requires specialized equipment and a dark room, making it inaccessible to most users. To avoid...","url_abs":"https://arxiv.org/abs/2510.03356","url_pdf":"https://arxiv.org/pdf/2510.03356v2","authors":"[\"Ziyang Chen\",\"Yuta Itoh\",\"Kaan Akşit\"]","published":"2025-10-02T23:11:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.ET\"]","methods":"[]","has_code":false}