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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

Showing 161170 of 517 papers

TitleStatusHype
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Learning for Disparity Estimation through Feature ConstancyCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
LeanStereo: A Leaner Backbone based Stereo NetworkCode0
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost VolumeCode0
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence LearningCode0
A Decomposition Model for Stereo MatchingCode0
Domain-invariant Stereo Matching NetworksCode0
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