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NSLF-OL

This repository contains the implementation of our RAL 2023 paper:

Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction

Yijun Yuan, Andreas Nüchter

Preprint | website

vis during traning

Di-Fusion's reconstruction + NSLF-OL's surface light fields

Come here to get this demo.

0. Install

conda create -n NSLF-OL python=3.8
conda activate NSLF-OL

pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116
pip install pygame==2.1.2 # dont 2.3.0, will cause problem!
pip install open3d numba opencv-python trimesh

1. Prepare data

Please edit the sequence path in [config.yaml] correspondingly!

2. How to use

2.1 Online learn the NSLF alongside Di-Fusion

python nslf_ol_vr.py [config.yaml]

Note that:

  • [config.yaml] examples are located in ./configs/
  • First time run will cause some time to compile c/cuda code, please use ps or top to find. Afterwards would be fast!
  • It will open a pygame window for visualization (240x320 by default, feel free to edit it in nslf_ol_vr.py:L137)

Please use keyboard

****w*****    ****^*****
**a*s*d***    ****|***** 
**********    <--*v*-->*

for turning and moving!

(pygame view will only change once keyboard control is raised.)

  • vis during train now only support non-thread inference.

2.2 Online learning without vis

python nslf_ol.py [config.yaml]
  • We also provide _nosurface.py for only nslf and _multiGPU.py for multiple GPUs.

2.3 Vis after train

python vr.py [config.yaml]
  • vis after train support multi-thread inference. Thus ought to be supper fast

3. Demo

python nslf_ol_vr.py configs/replica/replica_office0.yaml

or

python nslf_ol.py configs/replica/replica_office0.yaml
python vr.py configs/replica/replica_office0.yaml

4. TODO

  • Add data demo
  • Realize on-train visualization!
  • An easy to use nslf API to work in other reconstruction models.

Code contribute to this repository is always welcome!

Acknowledgement

This project is on top of Di-Fusion from Jiahui Huang, torch-ngp from Jiaxiang Tang. We thank for the open release of those contribution.

Citation

If you find this code or paper helpful, please cite:

@article{yuan2023online,
  title={Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction},
  author={Yuan, Yijun and N{\"u}chter, Andreas},
  journal={IEEE Robotics and Automation Letters},
  year={2023},
  publisher={IEEE}
}

Contact

Feel free to contact Yijun for any questions or comments. :D