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 191–200 of 517 papers

TitleStatusHype
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment—0
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
3D Scene Geometry Estimation from 360^ Imagery: A Survey—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
RomniStereo: Recurrent Omnidirectional Stereo MatchingCode0
LoS: Local Structure-Guided Stereo Matching—0
Show:102550
← PrevPage 20 of 52Next →

No leaderboard results yet.