OmniDepth-PyTorch

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

Dependencies

• PyTorch 1.0+ (choose the right install command here)
• numpy (pip install numpy or conda install -c anaconda numpy)
• scikit-image (pip install scikit-image or conda install -c conda-forge scikit-image)
• OpenEXR (pip install openexr, don't use the conda one, it never works. See note below if you have trouble.)
• visdom (pip install visdom)

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

Note: The OpenEXR dependency can be problematic to get set up. I've found that if the pip version isn't working right away, you should try installing the OpenEXR dev library first:

sudo apt install libopenexr-dev
pip install openexr

Cloning the network

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

git clone https://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.

Testing

Included is rectnet.pth, a PyTorch model converted from the released Caffe model for the top RectNet model from the original paper. If you have set everything up correctly, running test_omnidepth.py will run the evaluation script on this model and output the following results:

  Avg. Abs. Rel. Error: 0.0641
  Avg. Sq. Rel. Error: 0.0197
  Avg. Lin. RMS Error: 0.2297
  Avg. Log RMS Error: 0.0993
  Inlier D1: 0.9663
  Inlier D2: 0.9951
  Inlier D3: 0.9984

Training

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.

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, but I have not been able to match the Caffe results.

Credit

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

Zioulis, Nikolaos, et al. "OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas." 
Proceedings of the European Conference on Computer Vision (ECCV). 2018.