Stereo matching is one of the most important computer vision tasks. Several methods can be used to compute a matching cost of two pictures. This paper proposes a method that uses convolutional neural networks to compute the matching cost. The network architecture is described as well as teaching process. The matching cost metric based on the result of neural network is applied to base method which uses support points grid (ELAS). The proposed method was tested on Middlebury benchmark images and showed an accuracy improvement compared to the base method.
This paper proposes a stereo matching method that uses a support point grid in order to compute the prior disparity. Convolutional neural networks are used to compute the matching cost between pixels in two pictures. The network architecture is described as well as teaching process. The method was evaluated on Middlebury benchmark images. The results of accuracy estimation in case of using data from a LIDAR as an input for the support points grid is described. This approach can be used in multi-sensor devices and can give an advantage in accuracy up to 15%.