Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera

Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera

This paper presents a novel method to enhance ground truth disparity maps generated by Semi-Global Matching (SGM) using Maximum a Posteriori (MAP) estimation. SGM, while not producing visually appealing outputs like neural networks, offers high disparity accuracy in valid regions and avoids the gene...

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Bibliographic Details
Main Authors: Hanbit Gil, Sehyun Ryu, Sungmin Woo
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
stereo vision
deep learning
disparity map
MAP estimation
Semi-Global Matching
neural network
Online Access:https://www.mdpi.com/1424-8220/24/23/7761
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