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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 121130 of 517 papers

TitleStatusHype
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo MatchingCode1
GA-Net: Guided Aggregation Net for End-to-end Stereo MatchingCode1
Group-wise Correlation Stereo NetworkCode1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
Pyramid Stereo Matching NetworkCode1
SOS: Stereo Matching in O(1) with Slanted Support WindowsCode1
Stereo Matching by Training a Convolutional Neural Network to Compare Image PatchesCode1
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather0
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation0
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