Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Recent work has shown that CNN-based depth and ego-motion estimators can be learned
using unlabelled monocular videos. However, the performance is limited by unidentified …

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

Photometric loss is widely used for self-supervised depth and egomotion estimation.
However, the loss landscapes induced by photometric differences are often problematic for …

Self-supervised learning has shown very promising results for monocular depth estimation.
Scene structure and local details both are significant clues for high-quality depth estimation …

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and
outdoor robot navigation. In this work we address unsupervised learning of scene depth and …

We present an unsupervised learning framework for simultaneously training single-view
depth prediction and optical flow estimation models using unlabeled video sequences …

We present a novel method for simultaneous learning of depth, egomotion, object motion,
and camera intrinsics from monocular videos, using only consistency across neighboring …

Monocular depth estimation has become one of the most studied applications in computer
vision, where the most accurate approaches are based on fully supervised learning models …