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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
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-treesCode1
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo MatchingCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
MVPSNet: Fast Generalizable Multi-view Photometric StereoCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Stereo Plane SLAM Based on Intersecting LinesCode1
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
Depth-Based Selective Blurring in Stereo Images Using Accelerated FrameworkCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
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