The Plenoptic 2.0 Toolbox: Benchmarking of Depth Estimation Methods for MLA-Based Focused Plenoptic Cameras

摘要

MLA-based focused plenoptic cameras, also called type 2.0 cameras, have advantages over type 1.0 plenoptic cameras, because of their better inherent spatial image resolution and their compromise between depth of focus and angular resolution. However, they are more difficult to process since they require a depth estimation first to compute the all-in-focus image from the raw MLA image data. Current toolboxes for plenoptic cameras only support the type 1.0 cameras (like Lytro) and cannot handle type 2.0 cameras (like Raytrix). In addition, there is a lack of ground truth data and high quality benchmarking data for focussed plenoptic cameras. This contribution will discuss the requirements for processing type 2.0 images and will supply the reader with an open-source toolbox for comparing depth estimation methods. Different depth-estimation methods for MLA-based imaging will be available and an easy extension for other processing algorithms like compression will be included. In addition, we will supply benchmarking data of focused plenoptic cameras by synthetic ground truth datasets and high-quality real images captured under controlled conditions by Raytrix cameras.