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

Learning based methods have shown very promising results for the task of depth estimation
in single images. However, most existing approaches treat depth prediction as a supervised …

We present a novel approach for unsupervised learning of depth and ego-motion from
monocular video. Unsupervised learning removes the need for separate supervisory signals …

Despite learning based methods showing promising results in single view depth estimation
and visual odometry, most existing approaches treat the tasks in a supervised manner …

Wireless sensor networks are composed of large numbers of tiny networked devices that
communicate untethered. For large scale networks, it is important to be able to download …

Monocular visual odometry approaches that purely rely on geometric cues are prone to
scale drift and require sufficient motion parallax in successive frames for motion estimation …

We present a new, massively parallel method for high-quality multiview matching. Our work
builds on the Patchmatch idea: starting from randomly generated 3D planes in scene space …

In this paper, we propose the convolutional spatial propagation network (CSPN) and
demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

Current methods for single-image depth estimation use training datasets with real image-
depth pairs or stereo pairs, which are not easy to acquire. We propose a framework, trained …