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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 451460 of 517 papers

TitleStatusHype
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Active Event-based Stereo VisionCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event camerasCode0
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow ReconstructionCode0
Efficient Deep Learning for Stereo MatchingCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
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