maskpls
Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving, RA-L, 2023
Science Score: 65.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
○Academic publication links
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○Academic email domains
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✓Institutional organization owner
Organization prbonn has institutional domain (www.ipb.uni-bonn.de) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.5%) to scientific vocabulary
Repository
Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving, RA-L, 2023
Basic Info
Statistics
- Stars: 57
- Watchers: 2
- Forks: 8
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving
This repository contains the implementation of our paper.
Installation
- Install this package by running in the root directory of this repo:
pip3 install -U -e .
- Install the packages in requirements.txt.
Data preparation
SemanticKITTI
Download the SemanticKITTI dataset inside the directory data/kitti/. The directory structure should look like this:
./
└── data/
└── kitti
└── sequences
├── 00/
│ ├── velodyne/
| | ├── 000000.bin
| | ├── 000001.bin
| | └── ...
│ └── labels/
| ├── 000000.label
| ├── 000001.label
| └── ...
├── 08/ # for validation
├── 11/ # 11-21 for testing
└── 21/
└── ...
NuScenes
We use nuscenes2kitti to convert the nuScenes format into the SemanticKITTI format and store it in data/nuscenes/.
In the scripts, use the --nuscenes flag to train or evaluate using this dataset.
Pretrained models
Reproducing results
python3 scripts/evaluate_model.py --w [path_to_model]
Training
``` python3 scripts/train_model.py
```
Citation
bibtex
@article{marcuzzi2023ral,
author = {R. Marcuzzi and L. Nunes and L. Wiesmann and J. Behley and C. Stachniss},
title = {{Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving}},
journal = ral,
volume = {8},
number = {2},
pages = {1141--1148},
year = 2023,
doi = {10.1109/LRA.2023.3236568},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2023ral.pdf},
}
Licence
Copyright 2023, Rodrigo Marcuzzi, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file
Owner
- Name: Photogrammetry & Robotics Bonn
- Login: PRBonn
- Kind: organization
- Email: cyrill.stachniss@igg.uni-bonn.de
- Location: Bonn
- Website: www.ipb.uni-bonn.de
- Repositories: 41
- Profile: https://github.com/PRBonn
Photogrammetry & Robotics Lab at the University of Bonn
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
title: "Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving"
doi: "10.1109/LRA.2023.3236568"
year: "2023"
type: article
journal: "IEEE Robotics and Automation Letters (RA-L)"
url: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2023ral.pdf
codeurl: https://github.com/PRBonn/MaskPLS
authors:
- family-names: Marcuzzi
given-names: Rodrigo
- family-names: Nunes
given-names: Lucas
- family-names: Wiesmann
given-names: Louis
- family-names: Behley
given-names: Jens
- family-names: Stachniss
given-names: Cyrill
GitHub Events
Total
- Issues event: 3
- Watch event: 7
- Fork event: 2
Last Year
- Issues event: 3
- Watch event: 7
- Fork event: 2
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 21
- Total pull requests: 2
- Average time to close issues: 9 days
- Average time to close pull requests: 1 minute
- Total issue authors: 11
- Total pull request authors: 1
- Average comments per issue: 2.29
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 6
- Pull requests: 2
- Average time to close issues: 4 days
- Average time to close pull requests: 1 minute
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 0.33
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
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Pull Request Authors
- vuong-viet-hung (2)