In computer vision, edge and object contour detection is essential for higher-level vision
tasks, such as shape matching, visual salience, image segmentation, and object recognition …

Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …

Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …

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 an algorithm for reconstructing dense, geometrically consistent depth for all
pixels in a monocular video. We leverage a conventional structure-from-motion …