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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
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions—0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction—0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
The Sampling-Gaussian for stereo matching—0
Match Stereo Videos via Bidirectional Alignment—0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling—0
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching—0
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning—0
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching NetworksCode0
Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error PredictionCode0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework—0
Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images—0
Stereo Risk: A Continuous Modeling Approach to Stereo Matching—0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation—0
Rectified Iterative Disparity for Stereo Matching—0
SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo MatchingCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
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