# A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras

arXiv: https://arxiv.org/abs/1808.02244 · HTML: https://mlsanity.com/paper/1808.02244/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Leveraging blur information for plenoptic camera calibration](https://mlsanity.com/paper/2111.05226/) (2021)
- [Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images](https://mlsanity.com/paper/2311.01292/) (2023)
- [Light Field-Based Underwater 3D Reconstruction Via Angular Resampling](https://mlsanity.com/paper/2109.02116/) (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)
- [A Linear Fractional Transformation Model and Calibration Method for Light Field Camera](https://mlsanity.com/paper/2511.03962/) (2025)

## Abstract (arXiv)

Light field cameras can capture both spatial and angular information of light rays, enabling 3D reconstruction by a single exposure. The geometry of 3D reconstruction is affected by intrinsic parameters of a light field camera significantly. In the paper, we propose a multi-projection-center (MPC) model with 6 intrinsic parameters to characterize light field cameras based on traditional two-parallel-plane (TPP) representation. The MPC model can generally parameterize light field in different imaging formations, including conventional and focused light field cameras. By the constraints of 4D ray and 3D geometry, a 3D projective transformation is deduced to describe the relationship between geometric structure and the MPC coordinates. Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model. Our calibration method includes a close-form solution and a non-linear optimization by minimizing re-projection errors. Experimental results on both simulated and real scene data have verified the performance of our algorithm.