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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 151160 of 517 papers

TitleStatusHype
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo visionCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
DSR: Direct Self-rectification for Uncalibrated Dual-lens CamerasCode0
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