# Learned Display Radiance Fields with Lensless Cameras

arXiv: https://arxiv.org/abs/2510.03356 · HTML: https://mlsanity.com/paper/2510.03356/

## 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.

## Builds on (most cited first, 3 of 3)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [SpecTrack: Learned Multi-Rotation Tracking via Speckle Imaging](https://mlsanity.com/paper/2410.06028/) (2024)

## 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.