Adversarial Learning for Joint Optimization of Depth and Ego-Motion

In recent years, supervised deep learning methods have shown a great promise in dense depth estimation. However, massive high-quality training data are expensive and impractical to acquire. Alternatively, self-supervised learning-based depth estimators can learn the latent transformation from monocu...

Ausführliche Beschreibung