Learning Depth Cues from Focal Stack for Light Field Depth Estimation
Wenhui Zhou, Enci Zhou, Yuxiang Yan, Lili Lin, Andrew Lumsdaine · 2019
Deep neural networks have shown their excellent abilities in light field depth estimation. Most of learning based approaches focus on the depth feature extraction from the epipolar plane images (EPIs) or sub-apertures of light field, while pay less attention to the focal stack which is also one of the most distinctive characteristics of light field. In this paper, we propose a FocalStackNet which learns depth semantic features and local structure information from the focal stack for light field depth estimation. Specifically, we formulate the disparity estimation as a pixel-wise classification task, and discretize the continuous disparity range into 115 bins. Then we generate a discrete focal stack and extract a set of focal stack patches as training data. Finally, we train a two-pathway convolutional neural networks (CNN) to predict the disparity label of each pixel. Evaluation experiments are carried on the public 4D light field synthetic dataset. Our method achieves state-of-the-art performance. It ranks first among the published methods on the aspects of average and median error scores of Bad Pixel Ratio 0.03.