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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 4150 of 517 papers

TitleStatusHype
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Epipolar TransformersCode1
ES-Net: An Efficient Stereo Matching NetworkCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
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