We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field.

中文翻译：

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微透镜阵列网格估计、光场解码和校准

我们定量研究了用于基于微透镜阵列的光场相机的微透镜阵列网格估计的多种算法。明确考虑到自然和机械渐晕效应，我们提出了一种新的微透镜阵列网格估计方法，其性能优于之前在文献中讨论的方法。为了量化算法的性能，我们提出了一个评估管道，利用具有已知微透镜位置的特定于应用程序的光线追踪白色图像。使用合成白色图像的大型数据集，我们彻底比较了不同估计算法的性能。例如，我们将我们的结果应用于使用 Lytro Illum 相机拍摄的光场的解码和校准。我们观察到解码和校准受益于更准确的、渐晕感知的网格估计，