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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
Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging—0
Physics-based Differentiable Depth Sensor Simulation—0
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion—0
Playing to Vision Foundation Model's Strengths in Stereo Matching—0
Polarimetric PatchMatch Multi-View Stereo—0
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Pseudo-LiDAR for Visual Odometry—0
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching—0
Quantum Annealing for Computer Vision Minimization Problems—0
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids—0
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Real-Time Dense Stereo Embedded in A UAV for Road Inspection—0
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery—0
Real-Time High-Quality Stereo Matching System on a GPU—0
Real-Time Semantic Stereo Matching—0
Real-Time Stereo Vision for Road Surface 3-D Reconstruction—0
Real-Time Stereo Vision on FPGAs with SceneScan—0
Real-time Surface Deformation Recovery from Stereo Videos—0
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects—0
Rectified Iterative Disparity for Stereo Matching—0
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation—0
RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization—0
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