Discrete convolutional CRF networks for depth estimation from monocular infrared images

Predicting the depth of a scene from monocular infrared images, which plays a crucial role in understanding three-dimensional structures, is one of the challenging tasks in machine learning and computer vision. Considering the lack of texture and color information in infrared images, a novel discrete convolutional conditional random field network is proposed for depth estimation. The proposed method inherits several merits of conditional random fields and deep learning. First, the pairwise features are automatically extracted and optimized through deep architectures. Second, the monocular-images-based depth regression is converted into a multi-class classification, in which the order information of different levels of depths is considered in the loss function. Our experiments demonstrate that this conversion achieves much higher accuracy and faster conversion. Third, to obtain fine-grained level details, we have further proposed a multi-scale discrete convolutional conditional random field network that computes the pairwise features of the discrete conditional random field at different spatial levels. Extensive experiments on the infrared image dataset NUSTMS demonstrate that the proposed method outperforms other depth estimation methods. Specifically, for the proposed method, the mean relative error is 0.181, the mean log10 error is 0.072, and the accuracy with a threshold (t = 1.25³) is 95.3%.