关键词: 光场, 深度估计, 极平面图, 卷积神经网络, 数据增强, 密集连接型网络

Abstract:

To solve the problem of low accuracy and time-consuming computation in traditional depth estimation methods of light field, a densely connected CNN which has multiple input stream to estimate depth of light field is proposed. First, this algorithm preprocesses input images and converts them into Epipolar Plane Image volume（EPI）. Secondly, it uses light field specific data augmentation methods like random gray scale to overcome the lack of training data. Then, the CNN can convert all features into depth map. This algorithm uses shortcuts structures to reduce computation. Experimental results on HCI 4D light field dataset show that densely connected CNN can achieve better results in mean squared error and bad pixel ratio, and the efficiency is improved a lot.

Key words: light field, depth estimation, Epipolar Plane Image（EPI）, Convolutional Neural Network（CNN）, data augmentation, shortcuts