This code is tested with Ubuntu 16.04, Python3.6 and Pytorch 1.3, and CUDA 9.0.

sudo apt-get install libhdf5-10 libhdf5-serial-dev libhdf5-dev libhdf5-cpp-11
sudo apt install libopenexr-dev zlib1g-dev openexr
sudo apt install xorg-dev  # display widows
sudo apt install libglfw3-dev

1. Clone the repository. A small sample dataset of 3 real and 3 synthetic images is included.

   git clone <current-branch>

2. Install pip dependencies by running in terminal:

   pip install -r requirements.txt

3. Download the data:
   a) 数据集存储路径：

   

   b) Model Checkpoints (0.9GB) - (包含了masks、boundary和surface normal原模型) checkpoints位置在三个模型文件夹下的config目录中设置

   

4. Compile depth2depth (global optimization):

   depth2depth is a C++ global optimization module used for depth completion, adapted from the DeepCompletion project. It resides in the api/depth2depth/ directory.

   • To compile the depth2depth binary, you will first need to identify the path to libhdf5. Run the following command in terminal:

     find /usr -iname "*hdf5.h*"

     Note the location of hdf5/serial. It will look similar to: /usr/include/hdf5/serial/hdf5.h.

   • Edit BOTH lines 28-29 of the makefile at api/depth2depth/gaps/apps/depth2depth/Makefile to add the path you just found as shown below:

     USER_LIBS=-L/usr/include/hdf5/serial/ -lhdf5_serial
     USER_CFLAGS=-DRN_USE_CSPARSE "/usr/include/hdf5/serial/"

   • Compile the binary:

     cd api/depth2depth/gaps
     export CPATH="/usr/include/hdf5/serial/"  # Ensure this path is same as read from output of `find /usr -iname "*hdf5.h*"`
     
     make

     This should create an executable, api/depth2depth/gaps/bin/x86_64/depth2depth. The config files will need the path to this executable to run our depth estimation pipeline.

   • Check the executable, by passing in the provided sample files:

     cd api/depth2depth/gaps
     bash depth2depth.sh

     This will generate gaps/sample_files/output-depth.png, which should match the expected-output-depth.png sample file. It will also generate RGB visualizations of all the intermediate files.

The folder pytorch_networks/ contains the code used to train the surface normals, occlusion boundary and semantic segmentation models.

• Go the to respective folder (eg: pytorch_networks/surface_normals) and create a local copy of the config file:

  cp config/config.yaml.sample config/config.yaml

• Edit the config.yaml file to fill in the paths to the dataset, select hyperparameter values, etc. All the parameters are explained in comments within the config file.

• Start training:

  python train.py -c config/config.yaml

• Eval script can be run by:

  python eval.py -c config/config.yaml