We present the full-resolution correspondence learning for cross-domain images, which aids
image translation. We adopt a hierarchical strategy that uses the correspondence from …

This paper presents HITNet, a novel neural network architecture for real-time stereo
matching. Contrary to many recent neural network approaches that operate on a full …

Deep learning approaches to optical flow estimation have seen rapid progress over the
recent years. One common trait of many networks is that they refine an initial flow estimate …

Abstract 3D reconstruction of scenes using multiple images, relying on robust
correspondence search and depth estimation, has been thoroughly studied for the two‐view …

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 …

We present an optical flow estimation approach that operates on the full four-dimensional
cost volume. This direct approach shares the structural benefits of leading stereo matching …

Automated 3D reconstruction based on satellite images has become a research hotspot at
the interdisciplinary of photogrammetry and computer vision. The 3D results based on …

As a key component in many computer vision systems, optical flow estimation, especially
with large displacements, remains an open problem. In this paper we present a simple but …

Optical flow estimation is one of the most studied problems in computer vision, yet recent
benchmark datasets continue to reveal problem areas of today's approaches. Occlusions …

Estimating the disparity and normal direction of one pixel simultaneously, instead of only
disparity, also known as 3D label methods, can achieve much higher subpixel accuracy in …