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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 5160 of 517 papers

TitleStatusHype
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching0
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching0
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo MatchingCode4
Disparity Estimation Using a Quad-Pixel SensorCode1
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning0
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching NetworksCode0
Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error PredictionCode0
LiDAR-Event Stereo Fusion with HallucinationsCode1
Gaussian Mixture based Evidential Learning for Stereo Matching0
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