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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 251–300 of 517 papers

TitleStatusHype
Confidence Inference for Focused Learning in Stereo Matching—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Cross-Modality 3D Object Detection—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Deep Stereo Matching with Dense CRF Priors—0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture—0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo—0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Depth From Semi-Calibrated Stereo and Defocus—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling—0
Depth Refinement for Improved Stereo Reconstruction—0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching—0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
DiffuVolume: Diffusion Model for Volume based Stereo Matching—0
Direct Depth Learning Network for Stereo Matching—0
Direction Matters: Depth Estimation With a Surface Normal Classifier—0
Direct Monocular Odometry Using Points and Lines—0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
Disjoint Pose and Shape for 3D Face Reconstruction—0
Displacement-Invariant Cost Computation for Efficient Stereo Matching—0
Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices—0
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration—0
Domain Generalized Stereo Matching via Hierarchical Visual Transformation—0
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios—0
DSVO: Direct Stereo Visual Odometry—0
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching—0
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection—0
EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual—0
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance—0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Entropy-difference based stereo error detection—0
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