FF-GAN: Feature Fusion GAN for Monocular Depth Estimation

Since the results of CNN methods for monocular depth estimation generally suffer the problem of visual dissatisfaction, we propose Feature Fusion GAN (FF-GAN) to address this issue. First, an end-to-end network based on encoder-decoder structure is proposed as the generator of FF-GAN, which can exploit the information of different scales. The encoder of our generator fuse features in different levels with a feature fusion module. The component which can obtain the information of multi-scale receptive field is the main part of the decoder of our generator. Second, in order to match the generator, the discriminator of FF-GAN is designed to efficiently learn the information of different scales by applying pyramid structure. Experiments on public datasets demonstrate the effectiveness of our generator and discriminator. Compared with the CNN methods, the results predicted by FF-GAN are significantly improved in terms of texture loss and edge blur while ensuring accuracy, and the visual effect is better.