https://github.com/IRMVLab/DDS-SLAM
official code for DDS-SLAM: Dense Semantic Neural SLAM for Deforming Endoscopic Scenes
https://github.com/IRMVLab/DDS-SLAM
Last synced: 13 days ago
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official code for DDS-SLAM: Dense Semantic Neural SLAM for Deforming Endoscopic Scenes
- Host: GitHub
- URL: https://github.com/IRMVLab/DDS-SLAM
- Owner: IRMVLab
- Created: 2024-03-03T14:19:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-03T14:20:08.000Z (over 1 year ago)
- Last Synced: 2024-09-25T11:03:39.658Z (about 1 year ago)
- Homepage:
- Size: 1000 Bytes
- Stars: 5
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-NeRF-and-3DGS-SLAM - Paper
README
## Installation
```
# Create conda environment
conda create -n ddsslam python=3.7
conda activate ddsslam
# Install the pytorch first (Please check the cuda version)
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install -r requirements.txt
# Build extension (marching cubes from neuralRGBD)
cd external/NumpyMarchingCubes
python setup.py install
```
## Run
## Semantic-Super Dataset
The data can be found on [Data](https://drive.google.com/file/d/1ZxWw2kNmgeMhBXAGyovL2icXHzn2OCVV/view?usp=sharing). Then utilize the pre-trained depth estimation models provided by [Semantic-SuPer](https://github.com/ucsdarclab/Python-SuPer) to obtain deep priors.
Next, run DDS-SLAM
```
python ddsslam.py --config ./configs/Super/trail3.yaml
```
### StereoMIS Dataset
The data can be found [here](https://zenodo.org/records/7727692). Then utilize the pre-trained depth estimation models provided by [robust-pose-estimator](https://github.com/aimi-lab/robust-pose-estimator) to obtain deep priors.
Next, run DDS-SLAM
```
python ddsslam.py --config ./configs/StereoMIS/p2_1.yaml
```
## Related Repositories
Our codebase is based on [Co-SLAM](https://github.com/HengyiWang/Co-SLAM). We are grateful to the authors for sharing their codebase with the public. Your significant contributions have been instrumental in making our work a reality!
## Citation
If you find our code or paper useful, please cite
```
@inproceedings{shan2024dds,
title={DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscopic Scenes},
author={Shan, Jiwei and Li, Yirui and Yang, Lujia and Feng, Qiyu and Han, Lijun and Wang, Hesheng},
booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={10837--10842},
year={2024},
organization={IEEE}
}
```