Presentation 2020-12-10
Study on Depth Estimation from 4D Light Field Videos
Takahiro Kinoshita, Satoshi Ono,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) 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 4D LF videos dataset that can be used for training deep learning-based methods. Experimental results have shown that temporal information contributes to the improvement of depth estimation accuracy in noisy regions.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) light field video / light field dataset / depth estimation / deep neural network
Paper # AI2020-1
Date of Issue 2020-12-03 (AI)

Conference Information
Committee AI
Conference Date 2020/12/10(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online and HAMAMATSU ACT CITY
Topics (in Japanese) (See Japanese page)
Topics (in English) Foundations and application technologies for AI systems on the new normal
Chair Naoki Fukuta(Shizuoka Univ.)
Vice Chair Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST)
Secretary Yuichi Sei(Nagoya Inst. of Tech.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology)
Assistant

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Study on Depth Estimation from 4D Light Field Videos
Sub Title (in English)
Keyword(1) light field video
Keyword(2) light field dataset
Keyword(3) depth estimation
Keyword(4) deep neural network
1st Author's Name Takahiro Kinoshita
1st Author's Affiliation Kagoshima University(Kagoshima Univ.)
2nd Author's Name Satoshi Ono
2nd Author's Affiliation Kagoshima University(Kagoshima Univ.)
Date 2020-12-10
Paper # AI2020-1
Volume (vol) vol.120
Number (no) AI-281
Page pp.pp.1-6(AI),
#Pages 6
Date of Issue 2020-12-03 (AI)