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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
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Lightweight Multi-Drone Detection and 3D-Localization via YOLOCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
Deep 3D Portrait from a Single ImageCode1
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
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