  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
