Code for the TCI 2020 Paper

Multi-Angular Epipolar Geometry Based Light Field Angular Reconstruction Network

This code borrows heavily from the paper:

Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues

Description

This code is used to generate an 7 x 7 densely-sampled LF with 3 x 3 LF views as inputs

Requirements and Dependencies

• MATLAB
• cuda and cudnn (For GPU. Please modify install.m if not using cudnn)
• matconvnet (Please use the matconvnet code given in this repository. It contains the 3D convolution code written by authors)

Training

Set the training and validation data directory (opts.test_dir) in init_opts.m. Download the training and validation datasets to the specofoc directories. Make sure that there are enough memory for loading the whole training and validatoin datasets.

>> train

Testing Pretrained Models

Set the testing data directory (opts.test_dir) in init_opts.m

>> test

Testing Your Own Models

>> test_model(name, depth, gpu, saveImg, epoch, len)

• model_name : model name
• depth : model depth
• gpu : GPU ID
• saveImg : Save the HR SAIs if true
• epoch : model epoch to test
• len : controls the size of the sub-lightfield, value depends on GPU memory

Authors of the Paper

Deyang Liu , Yan Huang , Qiang Wu , Ran Ma, and Ping An