ashishd / rpg_e2depth

Code for Learning Monocular Dense Depth from Events paper (3DV20)

Learning Monocular Dense Depth from Events

This is the code for the paper Learning Monocular Dense Depth from Events by Javier Hidalgo-Carrió, Daniel Gehrig, and Davide Scaramuzza:

You can find a pdf of the paper here. If you use any of this code or our event camera plugin in CARLA, please cite the following publication:

@Article{Hidalgo20threedv,
  author        = {Javier Hidalgo-Carrio, Daniel Gehrig and Davide Scaramuzza},
  title         = {Learning Monocular Dense Depth from Events},
  journal       = {{IEEE} International Conference on 3D Vision.(3DV)},
  url           = {http://rpg.ifi.uzh.ch/docs/3DV20_Hidalgo.pdf},
  year          = 2020
}

Install

Dependencies:

• PyTorch >= 1.0
• NumPy
• OpenCV
• Matplotlib

Install with Anaconda

The installation requires Anaconda3. You can create a new Anaconda environment with the required dependencies as follows (make sure to adapt the CUDA toolkit version according to your setup):

conda create -n E2DEPTH
conda activate E2DEPTH
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
conda install -c conda-forge opencv
conda install -c conda-forge matplotlib

Run

• Download the pretrained model:

wget "http://rpg.ifi.uzh.ch/data/E2DEPTH/models/E2DEPTH_si_grad_loss_mixed.pth.tar" -O pretrained/E2DEPTH_si_grad_loss_mixed.pth.tar

• Download the test sequence in the DENSE dataset:

wget "http://rpg.ifi.uzh.ch/data/E2DEPTH/dataset/test_sequence_00_town10.zip" -O data/test_sequence_00_town10.zip

• Extract the data sequence:

unzip -q data/test_sequence_00_town10.zip -d data/test

Before running the depth prediction, make sure the conda environment is sourced:

conda activate E2DEPTH

• Run reconstruction:

python run_depth.py -c pretrained/E2DEPTH_si_grad_loss_mixed.pth.tar \
  -i data/test/events/voxels \
  --output_folder /tmp \
  --save_numpy \
  --show_event \
  --display \
  --save_inv_log \
  --save_color_map

Parameters

Below is a description of the most important parameters:

Main parameters

Output parameters

• --output_folder: path of the output folder. If not set, the image reconstructions will not be saved to disk.
• --dataset_name: name of the output folder directory (default: 'reconstruction').

Display parameters

• --display (default: False): display the video reconstruction in real-time in an OpenCV window.
• --show_events (default: False): show the input events side-by-side with the reconstruction. If --output_folder is set, the previews will also be saved to disk in /path/to/output/folder/events.
• --save_inv_log (default: False): compute (and then save) the inverse depth log instead of the depth log (default).
• --save_color_map (default: False): use color conding to display depth. It uses matplotlib 'magma' color map. Grayscale depth otherwise.

Display window

You can select between direct or inverse depth, log or lineal, and grayscale or color visualization.

DENSE dataset

We provide Depth Estimation oN Synthetic Events (DENSE) Dataset that you can use to train your model.

• DENSE

Event Camera plugin

You can extend DENSE or create your own dataset using our Event camera plugin. You can have a look here for a detailed documentation.

Acknowledgements

We thank Alessio Tonioni and Federico Tombari for their valuable help in this work.

This code borrows from the following open source projects, whom we would like to thank:

• pytorch-template
• E2VID