OmniDepth-PyTorch

A PyTorch reimplementation of the Omnidepth paper from Zioulis et al., ECCV 2018:

Notable difference with the paper: PyTorch's weight decay for the Adam solver does not seem to function the same way as Caffe's. Hence, I do not use weight decay in training. Instead, I use a learning rate schedule

Dependencies

PyTorch 1.0+
numpy (various things)
scikit-image (for data loading)
OpenEXR (for loading depth files)
visdom (for visualizations)

The easiest way to get set up is to just set up a conda environment using the omnidepth.yml file in this repository.

Cloning the network

Once you've installed and activated the conda environment, you should clone the network. You can do that with:

git clone git@github.com:meder411/omnidepth-pytorch.git

Dataset

To get the OmniDepth dataset, please file a request with the authors here.

Usage

Run python train_omnidepth.py to run the training routine. Run python test_omnidepth.py to run the evaluation routine. You can edit the parameters in those files.

During training, you first need to start the visdom server in order to visualize training. It's best to do this in a screen. To start the visdom server just call visdom.

The visualizations can be viewed at localhost:8097. If running visdom and the network on a server, you will need to tunnel to the server to view it locally. For example:

ssh -N -L 8888:localhost:8097 <username>@<server-ip>

allows you to view the training visualizations at localhost:8888 on your machine.

Credit

If you do use this repository, please make sure to cite the authors' original paper: