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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

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Showing 421–430 of 517 papers

TitleStatusHype
EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual—0
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance—0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps—0
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
End-to-End 3D Hand Pose Estimation from Stereo Cameras—0
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences—0
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo—0
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching—0
Entropy-difference based stereo error detection—0
Epipolar Geometry Based On Line Similarity—0
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