Depth estimation of a single RGB image with semisupervised two-stage regression

Obtaining accurate depth estimation at low computational cost is a major problem in the field of computer vision. To tackle this problem, we propose a framework that integrates different neural networks, for predicting the corresponding depth from a single RGB image and sparse depth samples. This method combines two different types of deep learning frameworks with the best performance, including the improved Residual Neural Network and conditional generation adversarial network (cGAN). It has been proved that the improved ResNet has strong depth prediction capability, but the depth map is still incomplete in detail. We improve the existing cGAN model to enhance ResNet-based depth prediction. Experiments compared with stage-of-the-art are performed on publicly available data sets. And the results demonstrate that the proposed two-stage deep regression model is superior to other existing methods of the same type.