Learned Display Radiance Fields with Lensless Cameras
This addresses the issue of display calibration for content creators by making it more accessible without specialized equipment, though it appears incremental as an initial step.
The paper tackles the problem of display calibration by co-designing a lensless camera and an Implicit Neural Representation algorithm to capture display characteristics from various viewpoints, achieving efficient reconstruction of light fields from a viewing cone of 46.6° × 37.6°.
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.