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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 8190 of 517 papers

TitleStatusHype
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented FeatureCode1
Group-wise Correlation Stereo NetworkCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image MatchingCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
Learning Stereo from Single ImagesCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
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