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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 151–175 of 517 papers

TitleStatusHype
Color Agnostic Cross-Spectral Disparity EstimationCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo visionCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Efficient stereo matching on embedded GPUs with zero-means cross correlationCode0
Efficient Deep Learning for Stereo MatchingCode0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
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
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Binary Stereo MatchingCode0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
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