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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 451–475 of 517 papers

TitleStatusHype
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing—0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Active Event-based Stereo VisionCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event camerasCode0
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionCode0
Efficient Deep Learning for Stereo MatchingCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Zoom and Learn: Generalizing Deep Stereo Matching to Novel DomainsCode0
Uncertainty Quantification in Stereo MatchingCode0
Cross-Scale Cost Aggregation for Stereo MatchingCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Continuous 3D Label Stereo Matching using Local Expansion MovesCode0
Continual Stereo Matching of Continuous Driving Scenes With Growing ArchitectureCode0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
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
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