Abstract

Convolutional Neural Networks (CNNs) have recently been successfully applied to various Computer Vision (CV) applications. In this paper we utilize CNNs to predict depth information for given Light Field (LF) data. The proposed method learns an end-to-end mapping between the 4D light fifield and a representation of the corresponding 4D depth fifield in terms of 2D hyperplane orientations. The obtained prediction is then further refifined in a post processing step by applying a higher-order regularization. Existing LF datasets are not suffificient for the purpose of the training scheme tackled in this paper. This is mainly due to the fact that the ground truth depth of existing datasets is inaccurate and/or the datasets are limited to a small number of LFs. This made it necessary to generate a new synthetic LF dataset, which is based on the raytracing software POV-Ray. This new dataset provides flfloating point accurate ground truth depth fifields, and due to a random scene generator the dataset can be scaled as required