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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 471–480 of 517 papers

TitleStatusHype
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost VolumeCode0
Domain-invariant Stereo Matching NetworksCode0
Computing the Stereo Matching Cost with a Convolutional Neural NetworkCode0
Learning for Disparity Estimation through Feature ConstancyCode0
Binary Stereo MatchingCode0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
LeanStereo: A Leaner Backbone based Stereo NetworkCode0
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
Color Agnostic Cross-Spectral Disparity EstimationCode0
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