A Linear Fractional Transformation Model and Calibration Method for Light Field Camera

TL;DR

This paper introduces a linear fractional transformation model for light field camera calibration, providing an analytical solution and nonlinear refinement to improve 3D reconstruction accuracy and speed up raw image simulation.

Contribution

It proposes a novel LFT-based calibration method with an analytical solution and feature detection technique, enhancing calibration efficiency and simulation speed.

Findings

Verified on physical and simulated data

Faster raw light field image simulation

Improved calibration accuracy

Abstract

Accurate calibration of internal parameters is a crucial yet challenging prerequisite for 3D reconstruction using light field cameras. In this paper, we propose a linear fractional transformation(LFT) parameter $\alpha$ to decoupled the main lens and micro lens array (MLA). The proposed method includes an analytical solution based on least squares, followed by nonlinear refinement. The method for detecting features from the raw images is also introduced. Experimental results on both physical and simulated data have verified the performance of proposed method. Based on proposed model, the simulation of raw light field images becomes faster, which is crucial for data-driven deep learning methods. The corresponding code can be obtained from the author's website.