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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

Showing 451500 of 517 papers

TitleStatusHype
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms0
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching0
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation0
CATS: A Color and Thermal Stereo Benchmark0
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems0
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly0
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance0
Accurate Optical Flow via Direct Cost Volume Processing0
Direct Monocular Odometry Using Points and Lines0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture0
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 Priors0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo0
3D Hand Pose Tracking and Estimation Using Stereo Matching0
HyperDepth: Learning Depth From Structured Light Without Matching0
Rotational Crossed-Slit Light Field0
The Global Patch Collider0
Efficient Deep Learning for Stereo MatchingCode0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions0
Depth From Semi-Calibrated Stereo and Defocus0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching0
Epipolar Geometry Based On Line Similarity0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization0
A Closed-Form Solution to Tensor Voting: Theory and Applications0
Stereo Matching by Joint Energy Minimization0
CV-HAZOP: Introducing Test Data Validation for Computer Vision0
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing0
Mutual-Structure for Joint Filtering0
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation0
MAP Disparity Estimation Using Hidden Markov Trees0
Local Convolutional Features With Unsupervised Training for Image Retrieval0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution0
A Deep Visual Correspondence Embedding Model for Stereo Matching Costs0
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints0
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives0
Wide baseline stereo matching with convex bounded-distortion constraints0
A Weighted Sparse Coding Framework for Saliency Detection0
Leveraging Stereo Matching With Learning-Based Confidence Measures0
Direction Matters: Depth Estimation With a Surface Normal Classifier0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision0
Simultaneous Video Defogging and Stereo Reconstruction0
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos0
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