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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 471–480 of 517 papers

TitleStatusHype
Learning Dense Wide Baseline Stereo Matching for People—0
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching—0
Learning Parallax for Stereo Event-based Motion Deblurring—0
Learning Patch Reconstructability for Accelerating Multi-View Stereo—0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
Learning the Matching Function—0
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching—0
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps—0
Left-Right Comparative Recurrent Model for Stereo Matching—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
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