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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 351–375 of 517 papers

TitleStatusHype
Learning Dense Wide Baseline Stereo Matching for People—0
Real-Time Semantic Stereo Matching—0
Semantic Stereo Matching With Pyramid Cost Volumes—0
Object-Centric Stereo Matching for 3D Object Detection—0
Real-Time Variational Fisheye Stereo without Rectification and UndistortionCode0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchCode0
Adaptive Unimodal Cost Volume Filtering for Deep Stereo MatchingCode0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Appearance and Shape from Water Reflection—0
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching—0
Local Detection of Stereo Occlusion Boundaries—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios—0
Multi-Level Context Ultra-Aggregation for Stereo Matching—0
DISCO: Depth Inference from Stereo using Context—0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Guided Stereo MatchingCode0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images—0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model—0
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