Digital video stabilization is a challenging task that aims to transform a potentially shaky
video into a pleasant one by smoothing the camera trajectory. Despite the various works …

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

Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

We present an algorithm for estimating consistent dense depth maps and camera poses
from a monocular video. We integrate a learning-based depth prior, in the form of a …

As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …

Training deep networks for semantic segmentation requires large amounts of labeled
training data, which presents a major challenge in practice, as labeling segmentation masks …

This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …

The ubiquitous multi-camera setup on modern autonomous vehicles provides an opportunity
to construct surround-view depth. Existing methods, however, either perform independent …

Visual Simultaneous Localization and Mapping (VSLAM) has attracted considerable
attention in recent years. This task involves using visual sensors to localize a robot while …

Estimating the motion of the camera together with the 3D structure of the scene from a
monocular vision system is a complex task that often relies on the so-called scene rigidity …