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NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

This repository contains the implementation of our paper:

NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping (PDF)
Junyuan Deng, Qi Wu, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun, Guoqing Liu, Wenxian Yu and Ling Pei
If you use our code in your work, please star our repo and cite our paper.

@inproceedings{deng2023nerfloam,
      title={NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping}, 
      author={Junyuan Deng and Qi Wu and Xieyuanli Chen and Songpengcheng Xia and Zhen Sun and Guoqing Liu and Wenxian Yu and Ling Pei},
      booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
      year={2023}

}
<div align=center> <img src="./docs/NeRFLOAM.gif"> </div>

Overview

pipeline

Overview of our method. Our method is based on our neural SDF and composed of three main components:

Quatitative results

The reconstructed maps odomap_kitti The qualitative result of our odometry mapping on the KITTI dataset. From left upper to right bottom, we list the results of sequences 00, 01, 03, 04, 05, 09, 10.

The odometry results odo_qual The qualitative results of our odometry on the KITTI dataset. From left to right, we list the results of sequences 00, 01, 03, 04, 05, 07, 09, 10. The dashed line corresponds to the ground truth and the blue line to our odometry method.

Data

  1. Newer College real-world LiDAR dataset: website.

  2. MaiCity synthetic LiDAR dataset: website.

  3. KITTI dataset: website.

Environment Setup

To run the code, a GPU with large memory is preferred. We tested the code with RTX3090 and GTX TITAN.

We use Conda to create a virtual environment and install dependencies:

pip3 install -r requirements.txt
sh install.sh
torch.classes.load_library("third_party/sparse_octree/build/lib.xxx/svo.xxx.so")
patchwork_module_path ="/xxx/patchwork-plusplus/build/python_wrapper"

Demo

python demo/run.py configs/maicity/maicity_01.yaml

Note

git checkout subscene

Evaluation

Acknowledgement

Some of our codes are adapted from Vox-Fusion.

Contact

Any questions or suggestions are welcome!

Junyuan Deng: d.juney@sjtu.edu.cn and Xieyuanli Chen: xieyuanli.chen@nudt.edu.cn

License

This project is free software made available under the MIT License. For details see the LICENSE file.