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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 291–300 of 517 papers

TitleStatusHype
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios—0
DSVO: Direct Stereo Visual Odometry—0
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching—0
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection—0
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
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