# Microlens array grid estimation, light field decoding, and calibration

arXiv: https://arxiv.org/abs/1912.13298 · HTML: https://mlsanity.com/paper/1912.13298/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning](https://mlsanity.com/paper/2103.10179/) (2021)
- [Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane](https://mlsanity.com/paper/2312.01761/) (2023)
- [Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera](https://mlsanity.com/paper/2402.12891/) (2024)

## Abstract (arXiv)

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field.