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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 51–100 of 517 papers

TitleStatusHype
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Scene Completeness-Aware Lidar Depth Completion for Driving ScenarioCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo MatchingCode1
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain GeneralizationCode1
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty EstimationCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
LiDAR-Event Stereo Fusion with HallucinationsCode1
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
Deep 3D Portrait from a Single ImageCode1
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented FeatureCode1
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
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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