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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
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo MethodsCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Neural Rays for Occlusion-aware Image-based RenderingCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-treesCode1
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo MatchingCode1
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo MatchingCode1
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
LiDAR-Event Stereo Fusion with HallucinationsCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
MVPSNet: Fast Generalizable Multi-view Photometric StereoCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Epipolar TransformersCode1
On the confidence of stereo matching in a deep-learning era: a quantitative evaluationCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
Learning Stereo from Single ImagesCode1
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Deep 3D Portrait from a Single ImageCode1
Learning Stereo Matchability in Disparity Regression NetworksCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
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
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