Learned Display Radiance Fields with Lensless Cameras
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Abstract (arXiv)
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 46.6deg X 37.6deg. Our emerging pipeline paves the initial steps towards effortless display calibration and characterization.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 6 references that are in the map. First posted on arXiv 2 Oct 2025.
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3 of 6 references.
References · 3
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2022Unrolled Primal-Dual Networks for Lensless Camerascited by 8
- 2024SpecTrack: Learned Multi-Rotation Tracking via Speckle Imagingcited by 1
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