Single View Depth Estimation via Dense Convolution Network with Self-supervision

Abstract

Depth estimation from single image by deep learning is a hot topic of research nowadays. Existing methods mainly focus on learning neural network supervised by ground truth. This paper proposes a method for single view depth estimation based on convolution neural network with self-supervision. Firstly, a modified dense encoder-decoder architecture is employed to predict the disparity maps of image which can then be converted into depth and only one single image is fed to predict depth at test time. Secondly, the stereo pairs without ground truth are used as samples to generate supervision signals by synthesizing the predicted results during network training, which is referenced to network training in self-supervision manner. Finally, a novel loss function is defined which considers not only the similarity between the stereo and synthesized images, but also the inconsistency between the predicted disparities, which can decrease the influence of illumination of images. Experimental comparisons against the state-of-the-art both supervised and unsupervised methods on two public datasets prove that the proposed method performs very well for single view depth estimation.