Plenoptic cameras enable the capturing of spatial as well as angular color information which can be used for various applications among which are image refocusing and depth calculations. However, these cameras are expensive and research in this area currently lacks data for ground truth comparisons. In this work we describe a flexible, easy-to-use Blender model for the different plenoptic camera types which is on the one hand able to provide the ground truth data for research and on the other hand allows an inexpensive assessment of the cameras usefulness for the desired applications. Furthermore we show that the rendering results exhibit the same image degradation effects as real cameras and make our simulation publicly available.

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This paper introduces a reformulation of the extended Kalman Filter using the Gauss-Helmert model for least squares estimation. By proving the equivalence of both estimators it is shown how the methods of statistical analysis in least squares estimation can be applied to the prediction and update process in Kalman Filtering. Especially the efficient computation of the reliability (or redundancy) matrix allows the implementation of self supervising systems. As an application an unparameterized method for estimating the variances of the filters process noise is presented.

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