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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 451–500 of 517 papers

TitleStatusHype
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
CATS: A Color and Thermal Stereo Benchmark—0
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems—0
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance—0
Accurate Optical Flow via Direct Cost Volume Processing—0
Direct Monocular Odometry Using Points and Lines—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence LearningCode0
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise LabelingCode0
Deep Stereo Matching with Dense CRF Priors—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
3D Hand Pose Tracking and Estimation Using Stereo Matching—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
Rotational Crossed-Slit Light Field—0
The Global Patch Collider—0
Efficient Deep Learning for Stereo MatchingCode0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions—0
Depth From Semi-Calibrated Stereo and Defocus—0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching—0
Epipolar Geometry Based On Line Similarity—0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization—0
A Closed-Form Solution to Tensor Voting: Theory and Applications—0
Stereo Matching by Joint Energy Minimization—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing—0
Mutual-Structure for Joint Filtering—0
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation—0
MAP Disparity Estimation Using Hidden Markov Trees—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
A Deep Visual Correspondence Embedding Model for Stereo Matching Costs—0
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints—0
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
Wide baseline stereo matching with convex bounded-distortion constraints—0
A Weighted Sparse Coding Framework for Saliency Detection—0
Leveraging Stereo Matching With Learning-Based Confidence Measures—0
Direction Matters: Depth Estimation With a Surface Normal Classifier—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
Simultaneous Video Defogging and Stereo Reconstruction—0
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos—0
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