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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 301–325 of 517 papers

TitleStatusHype
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Entropy-difference based stereo error detection—0
Epipolar Geometry Based On Line Similarity—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—0
Expanding Sparse Guidance for Stereo Matching—0
Expansion of Visual Hints for Improved Generalization in Stereo Matching—0
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality—0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—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
FoggyStereo: Stereo Matching With Fog Volume Representation—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Generalizable Novel-View Synthesis using a Stereo Camera—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Genetic Stereo Matching Algorithm with Fuzzy Fitness—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Graph Cut based Continuous Stereo Matching using Locally Shared Labels—0
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