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 401–410 of 517 papers

TitleStatusHype
Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging—0
Physics-based Differentiable Depth Sensor Simulation—0
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion—0
Playing to Vision Foundation Model's Strengths in Stereo Matching—0
Polarimetric PatchMatch Multi-View Stereo—0
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Pseudo-LiDAR for Visual Odometry—0
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching—0
Quantum Annealing for Computer Vision Minimization Problems—0
Show:102550
← PrevPage 41 of 52Next →

No leaderboard results yet.