Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

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 …

We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for
high-resolution multi-view stereo. With high computation speed and low memory …

We propose a novel deep learning architecture for regressing disparity from a rectified pair
of stereo images. We leverage knowledge of the problem's geometry to form a cost volume …

Estimating depth from RGB images is a long-standing ill-posed problem, which has been
explored for decades by the computer vision, graphics, and machine learning communities …

Disparity estimation for binocular stereo images finds a wide range of applications.
Traditional algorithms may fail on featureless regions, which could be handled by high-level …

Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …

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 …

Stereo matching algorithms usually consist of four steps, including matching cost calculation,
matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN …

We introduce a novel framework for training deep stereo networks effortlessly and without
any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate …