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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 51–75 of 517 papers

TitleStatusHype
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Group-wise Correlation Stereo NetworkCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Epipolar TransformersCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
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