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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

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Showing 371–380 of 517 papers

TitleStatusHype
DISCO: Depth Inference from Stereo using Context—0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Guided Stereo MatchingCode0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images—0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model—0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Automated 3D recovery from very high resolution multi-view satellite images—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
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