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Bibliographic Details
Main Authors: Kinoshita, Takahiro, Ono, Satoshi
Format: Preprint
Published: 2020
Subjects:
Online Access:https://arxiv.org/abs/2012.03021
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author Kinoshita, Takahiro
Ono, Satoshi
author_facet Kinoshita, Takahiro
Ono, Satoshi
contents Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF images while not considering temporal information, i.e., LF videos. This paper proposes an end-to-end neural network architecture for depth estimation from 4D LF videos. This study also constructs a medium-scale synthetic 4D LF video dataset that can be used for training deep learning-based methods. Experimental results using synthetic and real-world 4D LF videos show that temporal information contributes to the improvement of depth estimation accuracy in noisy regions. Dataset and code is available at: https://mediaeng-lfv.github.io/LFV_Disparity_Estimation
format Preprint
id arxiv_https___arxiv_org_abs_2012_03021
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Depth estimation from 4D light field videos
Kinoshita, Takahiro
Ono, Satoshi
Computer Vision and Pattern Recognition
Machine Learning
Image and Video Processing
Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF images while not considering temporal information, i.e., LF videos. This paper proposes an end-to-end neural network architecture for depth estimation from 4D LF videos. This study also constructs a medium-scale synthetic 4D LF video dataset that can be used for training deep learning-based methods. Experimental results using synthetic and real-world 4D LF videos show that temporal information contributes to the improvement of depth estimation accuracy in noisy regions. Dataset and code is available at: https://mediaeng-lfv.github.io/LFV_Disparity_Estimation
title Depth estimation from 4D light field videos
topic Computer Vision and Pattern Recognition
Machine Learning
Image and Video Processing
url https://arxiv.org/abs/2012.03021