A significant amount of research in the field of stereo vision has been published in the past
decade. Considerable progress has been made in improving accuracy of results as well as …

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-
operative morphology and motion of soft-tissues. This information is prerequisite to the …

Leveraging on the recent developments in convolutional neural networks (CNNs), matching
dense correspondence from a stereo pair has been cast as a learning problem, with …

In the past year, convolutional neural networks have been shown to perform extremely well
for stereo estimation. However, current architectures rely on siamese networks which exploit …

This paper proposes a novel model and dataset for 3D scene flow estimation with an
application to autonomous driving. Taking advantage of the fact that outdoor scenes often …

This work investigates the estimation of dense three-dimensional motion fields, commonly
referred to as scene flow. While great progress has been made in recent years, large …

Recent convolutional neural networks, especially end-to-end disparity estimation models,
achieve remarkable performance on stereo matching task. However, existed methods, even …

Modern camera calibration and multiview stereo techniques enable users to smoothly
navigate between different views of a scene captured using standard cameras. The …

This paper describes a method for scene reconstruction of complex, detailed environments
from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to …

The accuracy of stereo matching has been greatly improved by using deep learning with
convolutional neural networks. To further capture the details of disparity maps, in this paper …