Stereo Matching
150 papers with code • 0 benchmarks • 19 datasets
Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.
Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
Benchmarks
These leaderboards are used to track progress in Stereo Matching
Libraries
Use these libraries to find Stereo Matching models and implementationsDatasets
Virtual KITTI
ETH3D
Middlebury 2014
DrivingStereo
Virtual KITTI 2
PST900
IMC PhotoTourism
3D Ken Burns
Middlebury 2005
CATS
Most implemented papers
Hierarchical Deep Stereo Matching on High-resolution Images
gengshan-y/high-res-stereo
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CVPR 2019
We explore the problem of real-time stereo matching on high-res imagery.
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
This can be attributed to the memory-consuming cost volume representation and inappropriate depth inference.
FADNet: A Fast and Accurate Network for Disparity Estimation
HKBU-HPML/FADNet
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Deep neural networks (DNNs) have achieved great success in the area of computer vision.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
gallenszl/pcwnet
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First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
Learning Stereo from Single Images
nianticlabs/stereo-from-mono
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ECCV 2020
We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.
SMD-Nets: Stereo Mixture Density Networks
fabiotosi92/SMD-Nets
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CVPR 2021
Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Stereo matching is a fundamental building block for many vision and robotics applications.
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching
The patch-based fast disparity searching algorithm is adopted for the rectified stereo images.
Robust Confidence Intervals in Stereo Matching using Possibility Theory
To the best of our knowledge, this is the first method creating disparity confidence intervals based on the cost volume.
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
