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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 6170 of 517 papers

TitleStatusHype
Do End-to-end Stereo Algorithms Under-utilize Information?Code1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Diving into the Fusion of Monocular Priors for Generalized Stereo MatchingCode1
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Epipolar TransformersCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
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