Awesome
DEPOCO
This repository implements the algorithms described in our paper Deep Compression for Dense Point Cloud Maps.
How to get started (using Docker)
Dependenices nvida-docker
Install nvida-docker and follow these instructions
Data
You can download the dataset from here and link the dataset to the docker container by configuring the Makefile
DATASETS=<path-to-your-data>
Building the docker container
For building the Docker Container simply run
make build
in the root directory.
Running the Code
The first step is to run the docker container:
make run
The following commands assume to be run inside the docker container.
Training
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>
Evaluation
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.
Plotting the results
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.
Pretrained models
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
.
How to get started (without Docker)
Installation
A list of all dependencies and install instructions can be derived from the Dockerfile.
Running the code
After installation the training and evaluation can be run as explained before.
Qualitative Results
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.
Acknowledgements
Big thanks to Ignacio Vizzo for supporting me with Docker!
Citation
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
}