This repository implements the algorithms described in our paper Deep Compression for Dense Point Cloud Maps.

Install nvida-docker and follow these instructions

You can download the dataset from here and link the dataset to the docker container by configuring the Makefile

DATASETS=<path-to-your-data>

For building the Docker Container simply run

make build

in the root directory.

The first step is to run the docker container:

make run

The following commands assume to be run inside the docker container.

For training a network we first have to create the config file with all the parameters. An example of this can be found in /depoco/config/depoco.yaml. Make sure to give each config file a unique experiment_id: ... to not override previous models. To train the network simply run

python3 trainer -cfg <path-to-your-config>

Evaluating the network on the test set can be done by:

python3 evaluate.py -cfg <path-to-your-config>

All results will be saved in a dictonary.

We can plot the quantitative results e.g. by using Jupyter-Lab. An example of this is provided in depoco/notebooks/visualize.ipynb. Jupyter-Lab can be started in the Docker container by:

jupyter-lab  --ip 0.0.0.0 --no-browser --allow-root

The 8888 port is forwarded which allows us to use it as if it would be on the host machine.

The config files and the pretrained weights of our models are stored in depoco/network_files/eX/. The results can be inspected by the jupyter notebook depoco/notebooks/visualize.ipynb.

A list of all dependencies and install instructions can be derived from the Dockerfile.

After installation the training and evaluation can be run as explained before.

Plotting the point clouds using open3d can be done by

pyhon3 evaluate -cfg <path-to-your-config>

This can not be done in the docker container and thus requires the installation on the local machine.

Big thanks to Ignacio Vizzo for supporting me with Docker!

If you use this library for any academic work, please cite the original paper.

@article{wiesmann2021ral,
author = {L. Wiesmann and A. Milioto and X. Chen and C. Stachniss and J. Behley},
title = {{Deep Compression for Dense Point Cloud Maps}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
volume = 6,
issue = 2,
pages = {2060-2067},
doi = {10.1109/LRA.2021.3059633},
year = 2021
}