Calibration and Auto-Refinement for Light Field Cameras

TL;DR

This paper introduces a calibration and refinement method for light field cameras that improves 3D scene reconstruction accuracy through pattern-based parameter extraction and nonlinear optimization, validated on real and synthetic data.

Contribution

It proposes a novel calibration and auto-refinement approach for light field cameras using pairwise pattern extraction and correspondence-based optimization.

Findings

Effective calibration and rectification demonstrated on real data

Improved 3D reconstruction accuracy shown in experiments

Method outperforms existing calibration techniques

Abstract

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.