Training Data for Stereo Matching Algorithms Based on Neural Networks and a Method for Data Evaluation

This paper addresses the problem of selecting data for learning stereo matching algorithms. The paper presents an overview of currently available learning datasets, including synthetic data and data from real environments. Stereo matching algorithms based on neural networks require high quality learning data in order to provide expected results, which have a form of disparity maps. There are hundreds of stereo matching algorithms for processing images from stereo cameras. However, a significant problem with this 3D data processing technology is that there is a relatively low availability of learning data with dense ground truth from real environments. The paper also introduces an evaluation method for estimating the quality of input data. The method considers features such as the quality of calibration, the density of ground truth, and the occurrence of occluded areas. The research presented in this paper is aimed at developing stereo matching methods applicable not only to benchmarks of this kind of algorithms but also to applications in real environments.