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Stereo Matching

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

Papers

Showing 76100 of 517 papers

TitleStatusHype
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Group-wise Correlation Stereo NetworkCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
Epipolar TransformersCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
Learning Stereo from Single ImagesCode1
Learning Stereo Matchability in Disparity Regression NetworksCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo MatchingCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
Neural Rays for Occlusion-aware Image-based RenderingCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
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