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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 481–490 of 517 papers

TitleStatusHype
Stereo Matching by Joint Energy Minimization—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing—0
Mutual-Structure for Joint Filtering—0
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation—0
MAP Disparity Estimation Using Hidden Markov Trees—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
A Deep Visual Correspondence Embedding Model for Stereo Matching Costs—0
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