Depth estimation from 4D light field videos

Takahiro Kinoshita; Satoshi Ono

doi:10.1117/12.2591012

13 March 2021 Depth estimation from 4D light field videos

Takahiro Kinoshita, Satoshi Ono

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117660A (2021) https://doi.org/10.1117/12.2591012
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

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. Our dataset and source codes are available at: https://mediaeng-lfv.github.io/LFV_Disparity_Estimation/.

Citation Download Citation

Takahiro Kinoshita and Satoshi Ono "Depth estimation from 4D light field videos", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117660A (13 March 2021); https://doi.org/10.1117/12.2591012

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