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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 151–200 of 517 papers

TitleStatusHype
CV-HAZOP: Introducing Test Data Validation for Computer Vision—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
Cross-Modality 3D Object Detection—0
Co-Teaching: An Ark to Unsupervised Stereo Matching—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Intention-driven Ego-to-Exo Video Generation—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles—0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
3D Scene Geometry Estimation from 360^ Imagery: A Survey—0
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method—0
Generalizable Novel-View Synthesis using a Stereo Camera—0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images—0
FPGA-based Binocular Image Feature Extraction and Matching System—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks—0
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—0
Inter-View Depth Consistency Testing in Depth Difference Subspace—0
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks—0
Consistency-aware Self-Training for Iterative-based Stereo Matching—0
Confidence Inference for Focused Learning in Stereo Matching—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
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
Expanding Sparse Guidance for Stereo Matching—0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map—0
Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision—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
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching—0
FoggyStereo: Stereo Matching With Fog Volume Representation—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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