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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

Showing 101110 of 517 papers

TitleStatusHype
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue0
3D Scene Geometry Estimation from 360^ Imagery: A Survey0
Left-right Discrepancy for Adversarial Attack on Stereo Networks0
RomniStereo: Recurrent Omnidirectional Stereo MatchingCode0
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar ImageryCode1
LoS: Local Structure-Guided Stereo Matching0
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids0
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
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