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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 251260 of 517 papers

TitleStatusHype
CATS: A Color and Thermal Stereo Benchmark0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation0
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching0
Comparison of Stereo Matching Algorithms for the Development of Disparity Map0
Confidence Inference for Focused Learning in Stereo Matching0
Consistency-aware Self-Training for Iterative-based Stereo Matching0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching0
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction0
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles0
Co-Teaching: An Ark to Unsupervised Stereo Matching0
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