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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 251–260 of 517 papers

TitleStatusHype
Stereo Matching Based on Visual Sensitive InformationCode1
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
A Decomposition Model for Stereo MatchingCode0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
Stereo Matching by Self-supervision of Multiscopic Vision—0
CFNet: Cascade and Fused Cost Volume for Robust Stereo MatchingCode1
SMD-Nets: Stereo Mixture Density NetworksCode1
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Physics-based Differentiable Depth Sensor Simulation—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
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