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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 281–290 of 517 papers

TitleStatusHype
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge—0
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual—0
Hierarchical Neural Architecture Search for Deep Stereo MatchingCode1
Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving—0
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery—0
Adaptive confidence thresholding for monocular depth estimationCode1
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