Calibration and Auto-Refinement for Light Field Cameras
This work addresses calibration challenges for light field cameras, which is an incremental improvement in a domain-specific area of computer vision.
The paper tackles the problem of calibrating and rectifying light field cameras by introducing a pairwise pattern-based parameter extraction method followed by a correspondence-based refinement algorithm using triangulation and nonlinear optimization. The result is validated on real and synthetic data, but no concrete numbers are provided.
The ability to create an accurate three-dimensional reconstruction of a captured scene draws attention to the principles of light fields. This paper presents an approach for light field camera calibration and rectification, based on pairwise pattern-based parameters extraction. It is followed by a correspondence-based algorithm for camera parameters refinement from arbitrary scenes using the triangulation filter and nonlinear optimization. The effectiveness of our approach is validated on both real and synthetic data.