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 …
The application of deep learning in stereo matching and disparity estimation: A bibliometric review
C Wang, X Cui, S Zhao, K Guo, Y Wang… - Expert Systems with …, 2023 - Elsevier
Estimating the depth of the 3D world from 2D images is a classic and important issue in
computer vision, which has been widely studied for decades. With the remarkable effect of …
computer vision, which has been widely studied for decades. With the remarkable effect of …
An efficient detection and classification of acute leukemia using transfer learning and orthogonal softmax layer-based model
For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of
blood cells is very important. Traditional deep CNNs lead to overfitting when it receives …
blood cells is very important. Traditional deep CNNs lead to overfitting when it receives …
Self-supervised multiscale adversarial regression network for stereo disparity estimation
Deep learning approaches have significantly contributed to recent progress in stereo
matching. These deep stereo matching methods are usually based on supervised training …
matching. These deep stereo matching methods are usually based on supervised training …
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 …
High-precision depth estimation using uncalibrated LiDAR and stereo fusion
We address the problem of 3D reconstruction from uncalibrated LiDAR point cloud and
stereo images. Since the usage of each sensor alone for 3D reconstruction has weaknesses …
stereo images. Since the usage of each sensor alone for 3D reconstruction has weaknesses …
Masked representation learning for domain generalized stereo matching
Recently, many deep stereo matching methods have begun to focus on cross-domain
performance, achieving impressive achievements. However, these methods did not deal …
performance, achieving impressive achievements. However, these methods did not deal …
Beyond local reasoning for stereo confidence estimation with deep learning
Confidence measures for stereo gained popularity in recent years due to their improved
capability to detect outliers and the increasing number of applications exploiting these cues …
capability to detect outliers and the increasing number of applications exploiting these cues …
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 …