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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 91100 of 517 papers

TitleStatusHype
Disparity Estimation Using a Quad-Pixel SensorCode1
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Multi-Label Stereo Matching for Transparent Scene Depth EstimationCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Neural Rays for Occlusion-aware Image-based RenderingCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
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