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 …

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 …

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 …

Learning-based multi-view stereo (MVS) methods have demonstrated promising results.
However, very few existing networks explicitly take the pixel-wise visibility into consideration …

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 …

We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We
propose a novel GRU-based estimator that encodes pixel-wise probability distributions of …

Learning-based multi-view stereo (MVS) methods have demonstrated promising results.
However, very few existing networks explicitly take the pixel-wise visibility into consideration …

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

We present a novel semi-supervised learning framework that intelligently leverages the
consistency regularization between the model's predictions from two strongly-augmented …

We present a new loss function for joint disparity and uncertainty estimation in deep stereo
matching. Our work is motivated by the need for precise uncertainty estimates and the …