https://github.com/prbonn/kppr
KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition
https://github.com/prbonn/kppr
Last synced: 4 months ago
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KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition
- Host: GitHub
- URL: https://github.com/prbonn/kppr
- Owner: PRBonn
- License: mit
- Created: 2022-12-07T10:06:47.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-08T14:06:59.000Z (almost 3 years ago)
- Last Synced: 2023-08-10T20:33:27.707Z (over 2 years ago)
- Language: Python
- Size: 19.5 KB
- Stars: 22
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition
## Installation
1. Install all requirements: `pip install -r requirements.txt`
2. Install this repository: `pip install -e .`
## Usage
### Training
All the following commands should be run in `kppr/`
- Please update the config files (especially the `oxford_data.yaml` to match your data_dir)
- Run the training: `python train.py`
- The output will be saved in `retriever/experiments/{EXPERIMENT_ID}`
### Testing
- Test the model by running: `python test.py --checkpoint {PATH/TO/CHECKPOINT.ckpt} --dataset {DATASET} --base_dir {PATH/TO/DATA}`, where `{DATASET}` is e.g. `oxford`
- The output will be saved in the same folder as the checkpoint
- All the results can be visualized with: `python scripts/vis_results.py`
- The numbers of the paper are in `experiments/kppr/.../oxford_evaluation_query.txt`
- The pre-trained model can be downloaded [here](https://www.ipb.uni-bonn.de/html/projects/kppr/kppr.ckpt) and should be placed into `experiments/kppr/lightning_logs/version_0/`.
## Data
- The pre-compressed point cloud maps can be downloaded [here](https://www.ipb.uni-bonn.de/html/projects/retriever/oxford_compressed.zip) and should be extracted to `data/` (or simply put a symbolic link).
- For the uncompressed point clouds, I refer to [PointNetVLAD](https://github.com/mikacuy/pointnetvlad).
## Citation
If you use this library for any academic work, please cite the original paper.
```bibtex
@article{wiesmann2023ral,
author = {L. Wiesmann and L. Nunes and J. Behley and C. Stachniss},
title = {{KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition}},
journal = ral,
volume = {8},
number = {2},
pages = {592-599},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2022.3228174},
}
```