## 2 Related Work
### Testbeds  
Testbeds for testing 3D vision algorithms used with stereo cameras are widely used for improving the stereo vision technology. As mentioned in the introduction, Middlebury Stereo Evaluation is one of the most important sources of data for testing stereo vision algorithms [10,35]. As of 6 August 2021, there are in total 71 datasets in the testbed. Each dataset consists of a pair of images and ground truth reflecting real values of disparities for all objects visible in images. The testbed was prepared by Middlebury College and Microsoft Research in cooperation with US National Science Foundation. Apart from test datasets, the project releases a ranking of stereo matching algorithms. The current, third version of the ranking evaluates over 100 algorithms.  
KITTI Vision Benchmark Suite is another evaluation system for stereo matching algorithms [36]. Datasets included in the benchmark consist of images taken from a stereo camera mounted on a car moving along streets. The testbed is foremost intended for selecting algorithms which are the most suitable for obtaining 3D data in autonomous cars. KITTI presents also a ranking of stereo matching algorithms. As of 6 August 2021, the ranking considers 316 algorithms.  
There is also a test data set called NYU Depth Dataset V2 ([[https://cs.nyu.edu/silberman/datasets/nyu_depth_v2.html](https://cs.nyu.edu/silberman/datasets/nyu_depth_v2.html)]([https://cs.nyu.edu/silberman/datasets/nyu_depth_v2.html](https://cs.nyu.edu/silberman/datasets/nyu_depth_v2.html)) (accessed on 9 August 2021)) [37]. The set contains images of indoor sites. Apart from testing the quality of depth estimation results the set was prepared for evaluating image segmentation algorithms.  
Moreover, the evaluation of stereo matching algorithms is possible with the use of ETH3D Benchmark ([[https://www.eth3d.net/](https://www.eth3d.net/)]([https://www.eth3d.net/](https://www.eth3d.net/)) (accessed on 9 August 2021)) [38]. The benchmark provides image pairs taken in different sites including indoor and outdoor areas. The benchmark also presents the ranking of matching methods.  
As far as test datasets for camera arrays are concerned, this kind of resources is only provided by the ETH3D Benchmark in a form of videos made with the use of a camera array consisting of 4 cameras placed along a line ([[https://www.eth3d.net/](https://www.eth3d.net/)]([https://www.eth3d.net/](https://www.eth3d.net/)) (accessed on 9 August 2021)) [38]. The rig was constructed from two stereo cameras placed side by side. Authors of ETH3D used the rig to prepare 10 videos of different locations. A sample set of four images taken with the use of the rig is presented in Figure 1. ETH3D is the only testbed currently available for testing algorithms taking advantage of camera arrays.  
There are also benchmarks for testing algorithms which obtain 3D scans from either a video recorded by moving camera or a set of images taken by a single camera from different points of view (multi-view stereo). Developing algorithms for these and other kinds of image processing is a subject of Robust Vision Challenge ([[http://www.robustvision.net/](http://www.robustvision.net/)]([http://www.robustvision.net/](http://www.robustvision.net/)) (accessed on 9 August 2021)). The challenge is supported by leading companies such as Intel, Google, Apple, Bosch and Daimler. However, neither the challenge nor other sources of data for 3D vision make it possible to perform tests with camera arrays presented in this paper.