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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 401–450 of 517 papers

TitleStatusHype
Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique—0
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery—0
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects—0
Learning Depth with Convolutional Spatial Propagation NetworkCode0
Semi-dense Stereo Matching using Dual CNNs—0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
Confidence Inference for Focused Learning in Stereo Matching—0
Real-Time Stereo Vision on FPGAs with SceneScan—0
DSVO: Direct Stereo Visual Odometry—0
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement—0
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization—0
Stereo 3D Object Trajectory Reconstruction—0
Stereo Computation for a Single Mixture Image—0
Multi-scale CNN stereo and pattern removal technique for underwater active stereo system—0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Open-World Stereo Video Matching with Deep RNN—0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth PredictionCode0
Real-Time Stereo Vision for Road Surface 3-D Reconstruction—0
Road surface 3d reconstruction based on dense subpixel disparity map estimationCode0
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching—0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships—0
Learning Patch Reconstructability for Accelerating Multi-View Stereo—0
Deep Material-Aware Cross-Spectral Stereo Matching—0
Fast Feature Extraction with CNNs with Pooling LayersCode0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation—0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Left-Right Comparative Recurrent Model for Stereo Matching—0
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching—0
Semantic See-Through Rendering on Light Fields—0
Robust Depth Estimation from Auto Bracketed Images—0
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model—0
Zoom and Learn: Generalizing Deep Stereo Matching to Novel DomainsCode0
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching—0
Single View Stereo MatchingCode0
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded DevicesCode0
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Learning for Disparity Estimation through Feature ConstancyCode0
Semi-Global Stereo Matching with Surface Orientation Priors—0
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs—0
Entropy-difference based stereo error detection—0
Robust Visual SLAM with Point and Line Features—0
Widening siamese architectures for stereo matching—0
Robust Pseudo Random Fields for Light-Field Stereo Matching—0
Unsupervised Learning of Stereo Matching—0
Virtual Blood Vessels in Complex Background using Stereo X-ray Images—0
Look Wider to Match Image Patches with Convolutional Neural Networks—0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Hyperspectral Light Field Stereo Matching—0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
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