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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

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Showing 101–125 of 517 papers

TitleStatusHype
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Group-wise Correlation Stereo NetworkCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
SOS: Stereo Matching in O(1) with Slanted Support WindowsCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
SRH-Net: Stacked Recurrent Hourglass Network for Stereo MatchingCode1
Epipolar TransformersCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth ConversionCode1
Stereo Matching Based on Visual Sensitive InformationCode1
Adaptive confidence thresholding for monocular depth estimationCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
Superpixel Segmentation with Fully Convolutional NetworksCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
When Epipolar Constraint Meets Non-local Operators in Multi-View StereoCode1
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