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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 221–230 of 517 papers

TitleStatusHype
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
A Closed-Form Solution to Tensor Voting: Theory and Applications—0
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas—0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching—0
CATS: A Color and Thermal Stereo Benchmark—0
Efficient High-Resolution Stereo Matching using Local Plane Sweeps—0
Deep Stereo Matching with Dense CRF Priors—0
Learning Adaptive Dense Event Stereo From the Image Domain—0
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