Light Field Depth Estimation Method Based on Encoder-decoder Architecture

Abstract

Aiming at the solution to the time-consuming and low-precision disadvantage of present methodologies,the light field depth estimation method combining context information of the scene is proposed.This method is based on an end-to-end convolutional neural network,with the advantage of obtaining depth map from a single light field image.On merit of the reduced computational cost from this method,the time consumption is consequently decreased.For improvement in calculation accuracy,multi orientation epipolar plane image volumes of the light field images are input to network,from which feature can be extracted by the multi-stream encoding module,and then aggregated by the encoding-decoding architecture with skip connection,resulting in fuse the context information of the neighborhood of the target pixel in the process of per-pixel disparity estimation.Furthermore,the model uses convolutional blocks of different depths to extract the structural features of the scene from the central viewpoint image,by introducing these structural features into the corresponding skip connection,additional references for edge features are obtained and the calculation accuracy is further improved.Experiments in the HCI 4D Light Field Benchmark show that the BadPix index and MSE index of the proposed method are respectively 31.2% and 54.6% lower than those of the comparison me-thod,and the average calculation time of depth estimation is 1.2 seconds,which is much faster than comparison method.

Keywords

• light field|depth estimation|epipolar plane image|encoder-decoder|context information