Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to
regress dense disparity maps from stereo pairs. These models, however, suffer from a …

Depth estimation from a single image represents a fascinating, yet challenging problem with
countless applications. Recent works proved that this task could be learned without direct …

State-of-the-art deep learning based stereo matching approaches treat disparity estimation
as a regression problem, where loss function is directly defined on true disparities and their …

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen
environments due to significant domain differences, such as color, illumination, contrast, and …

Although supervised deep stereo matching networks have made impressive achievements,
the poor generalization ability caused by the domain gap prevents them from being applied …

This paper proposes a novel framework integrating the principles of active stereo in
standard passive camera systems without a physical pattern projector. We virtually project a …

Scene understanding is paramount in robotics, self-navigation, augmented reality, and many
other fields. To fully accomplish this task, an autonomous agent has to infer the 3D structure …

Stereo matching aims to estimate the disparity between matching pixels in a stereo image
pair, which is important to robotics, autonomous driving, and other computer vision tasks …