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
ASV: Accelerated Stereo Vision SystemCode0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth From Semi-Calibrated Stereo and Defocus—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
A shallow feature extraction network with a large receptive field for stereo matching tasks—0
Show:102550
← PrevPage 20 of 52Next →

No leaderboard results yet.