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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 401–425 of 517 papers

TitleStatusHype
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
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
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
Deep Material-Aware Cross-Spectral Stereo Matching—0
Learning Patch Reconstructability for Accelerating Multi-View Stereo—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
Semantic See-Through Rendering on Light Fields—0
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