State-of-the-art stereo matching networks trained only on synthetic data often fail to
generalize to more challenging real data domains. In this paper, we attempt to unfold an …

Despite recent stereo matching networks achieving impressive performance given sufficient
training data, they suffer from domain shifts and generalize poorly to unseen domains. We …

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 …

Stereo is a prominent technique to infer dense depth maps from images, and deep learning
further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when …

Abstract Recently, deep Stereo Matching (SM) networks have shown impressive
performance and attracted increasing attention in computer vision. However, existing deep …

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound
progress in stereo matching. However, most of these successes are limited to a specific …

Recently, records on stereo matching benchmarks are constantly broken by end-to-end
disparity networks. However, the domain adaptation ability of these deep models is quite …

Recently, many deep stereo matching methods have begun to focus on cross-domain
performance, achieving impressive achievements. However, these methods did not deal …

Although convolutional neural network based stereo matching architectures have made
impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) …