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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 321–330 of 517 papers

TitleStatusHype
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100Code0
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Real-time Surface Deformation Recovery from Stereo Videos—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching—0
Visually Imbalanced Stereo Matching—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
WaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching—0
A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds—0
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