# LiFCal: Online Light Field Camera Calibration via Bundle Adjustment

arXiv: https://arxiv.org/abs/2408.11682 · HTML: https://mlsanity.com/paper/2408.11682/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras](https://mlsanity.com/paper/1708.07878/) (2017)
- [Occlusion-Aware Cost Constructor for Light Field Depth Estimation](https://mlsanity.com/paper/2203.01576/) (2022)
- [Self-Supervised Camera Self-Calibration from Video](https://mlsanity.com/paper/2112.03325/) (2021)
- [Blur Aware Calibration of Multi-Focus Plenoptic Camera](https://mlsanity.com/paper/2004.07745/) (2020)
- [Leveraging blur information for plenoptic camera calibration](https://mlsanity.com/paper/2111.05226/) (2021)
- [A Synchronized Stereo and Plenoptic Visual Odometry Dataset](https://mlsanity.com/paper/1807.09372/) (2018)

## Built on by (most cited first, 4 of 4)

- [LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration](https://mlsanity.com/paper/2510.13729/) (2025)
- [A Linear Fractional Transformation Model and Calibration Method for Light Field Camera](https://mlsanity.com/paper/2511.03962/) (2025)
- [PRISM-VO: Scale-Aware Visual Odometry Using Photometric Plenoptic Bundle Adjustment](https://mlsanity.com/paper/2607.00176/) (2026)
- [Beyond Reprojection Error: Camera Calibration with 3D Targets](https://mlsanity.com/paper/2608.05066/) (2026)

## Abstract (arXiv)

We propose LiFCal, a novel geometric online calibration pipeline for MLA-based light field cameras. LiFCal accurately determines model parameters from a moving camera sequence without precise calibration targets, integrating arbitrary metric scaling constraints. It optimizes intrinsic parameters of the light field camera model, the 3D coordinates of a sparse set of scene points and camera poses in a single bundle adjustment defined directly on micro image points. We show that LiFCal can reliably and repeatably calibrate a focused plenoptic camera using different input sequences, providing intrinsic camera parameters extremely close to state-of-the-art methods, while offering two main advantages: it can be applied in a target-free scene, and it is implemented online in a complete and continuous pipeline. Furthermore, we demonstrate the quality of the obtained camera parameters in downstream tasks like depth estimation and SLAM. Webpage: https://lifcal.github.io/