提出新模型，让光场相机标定更准更快
A Linear Fractional Transformation Model and Calibration Method for Light Field Camera
- 用单参数α解耦主镜头与微透镜阵列成像过程
- 均方误差仅2.1%，达到当前最优精度
- 适合做光场重建或相机标定的研究者
精确的内参标定是使用光场相机进行三维重建的关键挑战。现有标定模型通常将主镜头与微透镜阵列（MLA）联合分析，导致复杂度高、参数多。本文提出线性分式变换（LFT）模型，引入单一参数α，实现主镜头与MLA成像过程的解耦。设计专用矩阵𝐻𝛼刻画MLA投影，使两者可独立标定。所提方法包括𝐻𝛼的解析最小二乘解，以及所有内参的联合非线性优化。在真实数据集和模拟数据上的实验表明，该方法平均平移误差为2.1%，优于当前最优水平，同时保持亚像素重投影精度。完整代码库（含基于该模型的光场仿真器）已公开。
原文摘要 · Abstract (English)
Accurate intrinsic calibration is a crucial yet challenging prerequisite for 3D reconstruction using light field cameras. Existing calibration models typically analyze the main lens and micro lens array (MLA) in a coupled manner, resulting in high complexity and a large number of parameters. In this paper, we propose a linear fractional transformation (LFT) model that introduces a single parameter $α$ to decouple the imaging processes of the main lens and the MLA. A dedicated matrix $\mathbf{H}_α$ is designed to characterize the MLA projection, enabling the main lens and the MLA to be calibrated independently. The proposed calibration method consists of an analytical least-squares solution for $\mathbf{H}_α$, followed by joint nonlinear refinement of all intrinsic parameters. Experimental results on both physical datasets and simulated data demonstrate that the proposed method achieves a mean translation error of $2.1\%$, outperforming the state-of-the-art, while maintaining sub-pixel reprojection accuracy. The complete codebase, including a light field simulator based on the proposed model, is openly available to the research community.
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