LiFCal: Online Light Field Camera Calibration via Bundle Adjustment
This addresses the need for target-free calibration in light field cameras, offering an online solution for applications like depth estimation and SLAM, though it appears incremental as it builds on existing bundle adjustment techniques.
The paper tackles the problem of calibrating light field cameras without precise targets by proposing LiFCal, an online pipeline that uses bundle adjustment on moving sequences, achieving intrinsic parameters close to state-of-the-art methods.
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/