Efficient unsupervised monocular depth estimation using attention guided generative adversarial network

Deep-learning-based approaches to depth estimation are rapidly advancing, offering better performance over traditional computer vision approaches across many domains. However, for many critical applications, cutting-edge deep-learning based approaches require too much computational overhead to be operationally feasible. This is especially true for depth-estimation methods that leverage adversarial learning, such as Generative Adversarial Networks (GANs). In this paper, we propose a computationally efficient GAN for unsupervised monocular depth estimation using factorized convolutions and an attention mechanism. Specifically, we leverage the Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions (EESP) module of ESPNetv2 inside the network, leading to a total reduction of $$22.8\%$$ , $$35.37\%$$ , and $$31.5\%$$ in the number of model parameters, FLOPs, and inference time respectively, as compared to the previous unsupervised GAN approach. Finally, we propose a context-aware attention architecture to generate detail-oriented depth images. We demonstrate superior performance of our proposed model on two benchmark datasets KITTI and Cityscapes. We have also provided more qualitative examples (Fig. 8) at the end of this paper.