DGE-CNN demo for 2D-to-3D holographic display based on depth gradient extracting module and CNN network
"This source code is for training DGE-CNN network to recover depth map from single RGB 2D input."
"The output depth map is boundary-enhanced and will be used for layer-based angular spectrum calculation of 3D hologram."
"DGE module is packed in loss_gd in utils.py, to generate boundary-enhanced depth maps, train the network with loss_gd."
Author: Ninghe Liu (lnh20@mails.tsinghua.edu.cn)
run train.py to train the DGE-CNN network, you can choose the pretrained network we provided for easy convergence.
run inference.py to predict the boundary-enhanced depth map for input 2D image.
run segment.py to conduct pixel-average slicing according to predicted depth map and input 2D image.
The layer-based angular spectrum hologram generation is not available in this repository but may be obtained from the authors upon reasonable request.
Download the NYU Depth Dataset V2 Labelled Dataset : http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat.
Download the Make3D Dataset: http://make3d.cs.cornell.edu/data.html
Or we have provided our back up NYU Depth Dataset V2 data that goes with our demo.
Back up link (data and source code): https://cloud.tsinghua.edu.cn/d/edf599b1febf4c1a955e/
Ninghe Liu, Zhengzhong Huang, Zehao He, and Liangcai Cao, "DGE-CNN: 2D-to-3D holographic display based on a depth gradient extracting module and CNN network," Opt. Express 31, 23867-23876 (2023)
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