Convolutional neural network based deep conditional random fields for stereo matching
A deep CRF based stereo matching algorithm with CNN is proposed.The CNN potential function learns the potentials of CRF in a CNN framework.The inference of the deep CRF model is formulated as a Recurrent Neural Network.The deep CRF based algorithm outperforms other MRF based or CRF based methods. Stereo matching has been studied for many years and is still a challenge problem. The Markov Random Fields (MRF) model and the Conditional Random Fields (CRF) model based methods have achieved good performance recently. Based on these pioneer works, a deep conditional random fields based stereo matching algorithm is proposed in this paper, which draws a connection between the Convolutional Neural Network (CNN) and CRF. The object knowledge is used as a soft constraint, which can effectively improve the depth estimation accuracy. Moreover, we proposed a CNN potential function that learns the potentials of CRF in a CNN framework. The inference of the CRF model is formulated as a Recurrent Neural Network (RNN) A variety of experiments have been conducted on KITTI and Middlebury benchmark. The results show that the proposed algorithm can produce state of the art results and outperform other MRF based or CRF based methods.
J. Vis. Commun. Image Represent.