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 441–450 of 517 papers

TitleStatusHype
Robust Visual SLAM with Point and Line Features—0
Widening siamese architectures for stereo matching—0
Unsupervised Learning of Stereo Matching—0
Robust Pseudo Random Fields for Light-Field Stereo Matching—0
Virtual Blood Vessels in Complex Background using Stereo X-ray Images—0
Look Wider to Match Image Patches with Convolutional Neural Networks—0
Hyperspectral Light Field Stereo Matching—0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling—0
Show:102550
← PrevPage 45 of 52Next →

No leaderboard results yet.