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 426–450 of 517 papers

TitleStatusHype
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
Robust Depth Estimation from Auto Bracketed Images—0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
Robust Pseudo Random Fields for Light-Field Stereo Matching—0
Robust Visual SLAM with Point and Line Features—0
Rotational Crossed-Slit Light Field—0
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving—0
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction—0
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing—0
Segment-Tree Based Cost Aggregation for Stereo Matching—0
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets—0
Self-Supervised Intensity-Event Stereo Matching—0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Semantic See-Through Rendering on Light Fields—0
Semantic Stereo Matching With Pyramid Cost Volumes—0
Semi-dense Stereo Matching using Dual CNNs—0
Semi-Global Stereo Matching with Surface Orientation Priors—0
Semi-synthesis: A fast way to produce effective datasets for stereo matching—0
Show:102550
← PrevPage 18 of 21Next →

No leaderboard results yet.