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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 121130 of 517 papers

TitleStatusHype
Temporal Event Stereo via Joint Learning with Stereoscopic FlowCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Epipolar TransformersCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV ScenariosCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo MatchingCode1
Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution0
Depth From Semi-Calibrated Stereo and Defocus0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation0
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