https://github.com/prbonn/deep-point-map-compression
https://github.com/prbonn/deep-point-map-compression
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/prbonn/deep-point-map-compression
- Owner: PRBonn
- License: mit
- Created: 2020-12-21T16:08:58.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2023-02-13T16:42:52.000Z (over 3 years ago)
- Last Synced: 2025-04-13T14:47:11.852Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 4.97 MB
- Stars: 95
- Watchers: 11
- Forks: 28
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DEPOCO
This repository implements the algorithms described in our paper [Deep Compression for Dense Point Cloud Maps](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2021ral.pdf).
## How to get started (using Docker)
### Dependenices nvida-docker
Install nvida-docker and follow [these](https://stackoverflow.com/a/61737404)
instructions
## Data
You can download the dataset from [here](https://www.ipb.uni-bonn.de/html/projects/depoco/submaps.zip) and link the dataset to the docker container by configuring the Makefile
```sh
DATASETS=
```
## Building the docker container
For building the Docker Container simply run
```sh
make build
```
in the root directory.
## Running the Code
The first step is to run the docker container:
```sh
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
```sh
python3 trainer -cfg
```
### Evaluation
Evaluating the network on the test set can be done by:
```sh
python3 evaluate.py -cfg
```
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:
```sh
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
```sh
pyhon3 evaluate -cfg
```
This can **not** be done in the docker container and thus requires the installation on the local machine.
## Acknowledgements
Big thanks to [Ignacio Vizzo](https://github.com/nachovizzo) for supporting me with Docker!
## Citation
If you use this library for any academic work, please cite the original paper.
```bibtex
@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
}
```