caomw / dcnf-fcsp

Mirror of the dcnf-fcsp code: https://bitbucket.org/fayao/dcnf-fcsp

Depth from Single Monocular Images

• This is the prediction/test code for the paper:

Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields; available at: http://arxiv.org/abs/1502.07411.

• This code is tested on Ubuntu 14.04, and requires Matlab 2014a, CUDA 6.5 or later versions. Tested GPUs are NVIDIA Titan Black, K40c, GTX 780.

• Download this repository

• If this code is useful for your research, please consider to cite our work:

 @article{Depth2015Liu,
    author = {Fayao Liu and Chunhua Shen and Guosheng Lin  and Ian Reid},
    title  = {Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields},
    journal= {Technical report, University of Adelaide},
    volume = {},
    number = {},
    year   = {2015},
    url    = {http://arxiv.org/abs/1502.07411},
    month  = {},
    pages  = {},
}

Install

Two toolboxes are required for using this code. For convenience, they are included in the folder:

./libs and pre-compiled in Linux. These toolboxes are as follows:

1. MatConvNet is required for the CNN training, which can be downloaded at: http://www.vlfeat.org/matconvnet/

2. VLFeat is required for generating superpixels, which is available at http://www.vlfeat.org/. This code is tested using the VLFeat 0.9.18 version.

Run

1. Users need to compile MatConvNet before running our code. Please refer to: http://www.vlfeat.org/matconvnet/

2. We provide a demo file in folder ./demo/:

   demo_DCNF_FCSP_depths_prediction.m

   This is a demo for predicting depths of given images using our trained model.

3. We provide two trained models (trained using the Make3D and NYUD2 datasets respectively) in the folder ./model_trained.

Contact

authors: Fayao Liu, Chunhua Shen, Guosheng Lin, Ian Reid (University of Adelaide, Australia)

email: [email protected]

Copyright

Copyright (c) The authors, 2015.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

For commercial applications, please contact Chunhua Shen http://www.cs.adelaide.edu.au/~chhshen/.

03/2015