This repository represents the official implementation of the paper N3-Mapping:

@article{song2024n3,
  title={N $\^{}$\{$3$\}$ $-Mapping: Normal Guided Neural Non-Projective Signed Distance Fields for Large-scale 3D Mapping},
  author={Song, Shuangfu and Zhao, Junqiao and Huang, Kai and Lin, Jiaye and Ye, Chen and Feng, Tiantian},
  journal={IEEE Robotics and Automation Letters},
  year={2024},
  publisher={IEEE}
}

Installation

1. Clone the repository

git clone [email protected]:tiev-tongji/N3-Mapping.git
cd N3-Mapping

2. Set up conda environment

conda create --name n3 python=3.7
conda activate n3

3. Install the key requirement kaolin

Kaolin depends on Pytorch (>= 1.8, <= 1.13.1), please install the corresponding Pytorch for your CUDA version (can be checked by nvcc --version). You can find the installation commands here.

For example, for CUDA version >=11.6, you can use:

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

Kaolin now supports installation with wheels. For example, to install kaolin 0.13.0 over torch 1.12.1 and cuda 11.6:

pip install kaolin==0.13.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.1_cu116.html

4. Install the other requirements

pip install open3d scikit-image wandb tqdm natsort 

Run

Download the dataset the following script:

sh ./scripts/download_maicity.sh

Other datasets can also be downloaded in the same way:

sh ./scripts/download_ncd_example.sh
sh ./scripts/download_neural_rgbd_data.sh

The data should follow the kitti odometry format from here.

Therefore if you need to use Neural RGBD dataset, you can convert this dataset to the KITTI format by using for each sequence:

sh ./scripts/convert_rgbd_to_kitti_format.sh

Now we take the maicity as an example to show how to run the mapping system. First you need to check the config file such as ./config/maicity/maicity_incre.yaml and set the correct path like pc_path, pose_path and calib_path. Then use:

python run.py config/maicity/maicity_incre.yaml 

Evaluation

Please prepare your reconstructed mesh and corresponding ground truth point cloud. Then set the right data path and evaluation set-up in ./eval/evaluator.py. Now run:

python ./eval/evaluator.py

Contact

Feel free to contact me if you have any questions :)

• Song {[email protected]}

Acknowledgment

Our work is mainly built on SHINE-Mapping. Many thanks to the authors of this excellent work! We also appreciate the following great open-source works:

• Voxfield (comparison baseline, inspiration)
• Voxblox (comparison baseline)
• NeRF-LOAM (comparison baseline)
• Loc-NDF(inspiration)

TODO

Currently our implementation is more of a proof-of-concept and lacks optimization. We are working on improving this. A more efficient voxel-centric mapping design is on the way.