# Leveraging blur information for plenoptic camera calibration

arXiv: https://arxiv.org/abs/2111.05226 · HTML: https://mlsanity.com/paper/2111.05226/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 58 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Blur Aware Calibration of Multi-Focus Plenoptic Camera](https://mlsanity.com/paper/2004.07745/) (2020)
- [A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras](https://mlsanity.com/paper/1808.02244/) (2018)
- [Baseline and Triangulation Geometry in a Standard Plenoptic Camera](https://mlsanity.com/paper/2010.04638/) (2020)
- [Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration](https://mlsanity.com/paper/1608.04509/) (2016)

## Built on by (most cited first, 7 of 7)

- [LiFCal: Online Light Field Camera Calibration via Bundle Adjustment](https://mlsanity.com/paper/2408.11682/) (2024)
- [Blur aware metric depth estimation with multi-focus plenoptic cameras](https://mlsanity.com/paper/2308.04252/) (2023)
- [LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration](https://mlsanity.com/paper/2510.13729/) (2025)
- [Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera](https://mlsanity.com/paper/2402.12891/) (2024)
- [A Linear Fractional Transformation Model and Calibration Method for Light Field Camera](https://mlsanity.com/paper/2511.03962/) (2025)
- [CircleFlow: Flow-Guided Camera Blur Estimation using a Circle Grid Target](https://mlsanity.com/paper/2512.00796/) (2025)
- [PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment](https://mlsanity.com/paper/2607.00176/) (2026)

## Abstract (arXiv)

This paper presents a novel calibration algorithm for plenoptic cameras, especially the multi-focus configuration, where several types of micro-lenses are used, using raw images only. Current calibration methods rely on simplified projection models, use features from reconstructed images, or require separated calibrations for each type of micro-lens. In the multi-focus configuration, the same part of a scene will demonstrate different amounts of blur according to the micro-lens focal length. Usually, only micro-images with the smallest amount of blur are used. In order to exploit all available data, we propose to explicitly model the defocus blur in a new camera model with the help of our newly introduced Blur Aware Plenoptic (BAP) feature. First, it is used in a pre-calibration step that retrieves initial camera parameters, and second, to express a new cost function to be minimized in our single optimization process. Third, it is exploited to calibrate the relative blur between micro-images. It links the geometric blur, i.e., the blur circle, to the physical blur, i.e., the point spread function. Finally, we use the resulting blur profile to characterize the camera's depth of field. Quantitative evaluations in controlled environment on real-world data demonstrate the effectiveness of our calibrations.