On the synergies between machine learning and binocular stereo for depth estimation from images: a survey
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 …
years of studies and research. Throughout the years the paradigm has shifted from local …
Patchmatchnet: Learned multi-view patchmatch stereo
We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for
high-resolution multi-view stereo. With high computation speed and low memory …
high-resolution multi-view stereo. With high computation speed and low memory …
An overview of deep learning methods for image registration with focus on feature-based approaches
K Kuppala, S Banda, TR Barige - … Journal of Image and Data Fusion, 2020 - Taylor & Francis
Image registration is an essential pre-processing step for several computer vision problems
like image reconstruction and image fusion. In this paper, we present a review on image …
like image reconstruction and image fusion. In this paper, we present a review on image …
Adaptive unimodal cost volume filtering for deep stereo matching
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 …
as a regression problem, where loss function is directly defined on true disparities and their …
Learning the distribution of errors in stereo matching for joint disparity and uncertainty estimation
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 …
matching. Our work is motivated by the need for precise uncertainty estimates and the …
Pvsnet: Pixelwise visibility-aware multi-view stereo network
Recently, learning-based multi-view stereo methods have achieved promising results.
However, they all overlook the visibility difference among different views, which leads to an …
However, they all overlook the visibility difference among different views, which leads to an …
Unsupervised domain adaptation for depth prediction from images
State-of-the-art approaches to infer dense depth measurements from images rely on CNNs
trained end-to-end on a vast amount of data. However, these approaches suffer a drastic …
trained end-to-end on a vast amount of data. However, these approaches suffer a drastic …
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Stereo matching is one of the most popular techniques to estimate dense depth maps by
finding the disparity between matching pixels on two, synchronized and rectified images …
finding the disparity between matching pixels on two, synchronized and rectified images …
Laf-net: Locally adaptive fusion networks for stereo confidence estimation
We present a novel method that estimates confidence map of an initial disparity by making
full use of tri-modal input, including matching cost, disparity, and color image through deep …
full use of tri-modal input, including matching cost, disparity, and color image through deep …
Learning inverse depth regression for pixelwise visibility-aware multi-view stereo networks
Recently, learning-based multi-view stereo methods have achieved promising results.
However, most of them overlook the visibility difference among different views, which leads …
However, most of them overlook the visibility difference among different views, which leads …