This paper presents a novel technique for Shape from Light Field (SfLF), that utilizes deep learning strategies. Our model is based on a fully convolutional network, that involves two symmetric parts, an encoding and a decoding part, leading to a u-shaped network architecture. By leveraging a recently proposed Light Field (LF) dataset, we are able to effectively train our model using supervised training. To process an entire LF we split the LF data into the corresponding Epipolar Plane Image (EPI) representation and predict each EPI separately. This strategy provides good reconstruction results combined with a fast prediction time. In the experimental section we compare our method to the state of the art. The method performs well in terms of depth accuracy, and is able to outperform competing methods in terms of prediction time by a large margin.
|Title of host publication
|British Machine Vision Conference, BMVC 2016
|Published - Sept 2016