Adversarial Training for Unsupervised Monocular Depth Estimation
The problem of estimating scene-depth from a single image has seen great progress recently. It is one of the foundational problems in computer vision and hence been studied from various angles. Since the advent of deep learning, most of the approaches are data driven. These methods train high-capacity models with large amount of data in an end-to-end fashion. They rely on ground-truth depth, which is hard to capture and process. Recently self-supervised methods have been proposed which rely on view supervision as an alternative. These methods minimize photometric reconstruction error in order to learn depth. In this work,we propose a geometry-aware generative adversarial network to generate multiple novel views from a single image. Novel views are generated by learning depth as an intermediate step. The synthesized views are discerned from real images using discriminative learning. We show the gains of using the ad- versarial framework over previous methods. Furthermore, we present a structured adversarial training routine to train the network, going from easy examples to difficult ones. The combination of adversarial framework, multi-view learning, and structured training produces state-of-the-art performance on unsupervised depth estimation for monocular images. We also compare our method with human depth perception by conducting a series of experiments. We investigate the existence of monocular depth cues like relative size, occlusion and height in the visual field in artificial vision systems. With quantitative and qualitative experiments, we highlight the shortcomings of artificial depth perception and propose future avenues for research.
|Year of completion:||July 2019|
|Advisor :||P J Narayanan|