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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
Discrete Time Convolution for Fast Event-Based StereoCode1
Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene CompletionCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Structure Aggregation for Cross-Spectral Stereo Image Guided DenoisingCode1
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
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
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Learning Stereo Matchability in Disparity Regression NetworksCode1
OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye CamerasCode1
On the confidence of stereo matching in a deep-learning era: a quantitative evaluationCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
LightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy ImagesCode1
Neural Rays for Occlusion-aware Image-based RenderingCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency PerspectiveCode1
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
When Epipolar Constraint Meets Non-local Operators in Multi-View StereoCode1
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