SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 517 papers

TitleStatusHype
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
Show:102550
← PrevPage 20 of 21Next →

No leaderboard results yet.