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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 351–400 of 517 papers

TitleStatusHype
A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images—0
Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Analyzing Computer Vision Data - The Good, the Bad and the Ugly—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning—0
A novel stereo matching pipeline with robustness and unfixed disparity search range—0
An underwater binocular stereo matching algorithm based on the best search domain—0
A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery—0
A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle—0
A shallow feature extraction network with a large receptive field for stereo matching tasks—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Automated 3D recovery from very high resolution multi-view satellite images—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
A Weighted Sparse Coding Framework for Saliency Detection—0
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching—0
Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images—0
CATS: A Color and Thermal Stereo Benchmark—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
Confidence Inference for Focused Learning in Stereo Matching—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Cross-Modality 3D Object Detection—0
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Deep Stereo Matching with Dense CRF Priors—0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture—0
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo—0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Depth From Semi-Calibrated Stereo and Defocus—0
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling—0
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