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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 241250 of 517 papers

TitleStatusHype
High-Resolution Depth Maps Based on TOF-Stereo Fusion0
Neural Rays for Occlusion-aware Image-based RenderingCode1
Co-Teaching: An Ark to Unsupervised Stereo Matching0
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume0
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields0
Bilateral Grid Learning for Stereo Matching NetworksCode1
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning0
ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite ImagesCode1
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgeryCode0
SRH-Net: Stacked Recurrent Hourglass Network for Stereo MatchingCode1
Show:102550
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