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OmniDepth-PyTorch

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

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.

Note: The OpenEXR dependency can be problematic to get set up. I've found that if the pip version isn't working, installing via sudo apt install openexr often solves the problem when using Ubuntu.

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.