Structured Adversarial Training for Unsupervised Monocular Depth Estimation

Abstract

The problem of estimating scene-depth from a single image has seen great progress lately. Recent unsupervised methods are based on view-synthesis and learn depth by minimizing photometric reconstruction error. In this paper, we introduce Structured Adversarial Training (StrAT) to this problem. We generate multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order. Adversarial training that goes from easy examples to harder ones produces richer losses and better models. The impact of StrAT is shown to exceed traditional data augmentation using random new views. The combination of an adversarial framework, multiview learning, and structured adversarial training produces state-of-the-art performance on unsupervised depth estimation for monocular images. The StrAT framework can benefit several problems that use adversarial training.