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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 176–200 of 517 papers

TitleStatusHype
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
Hyperspectral Light Field Stereo Matching—0
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality—0
Expansion of Visual Hints for Improved Generalization in Stereo Matching—0
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
Expanding Sparse Guidance for Stereo Matching—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction—0
Fine-tuning deep learning models for stereo matching using results from semi-global matching—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
High-Resolution Depth Maps Based on TOF-Stereo Fusion—0
A Learned Stereo Depth System for Robotic Manipulation in Homes—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
A Comparative Evaluation of SGM Variants (including a New Variant, tMGM) for Dense Stereo Matching—0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
Epipolar Geometry Based On Line Similarity—0
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