Skip to content

This repository is home to the code for the paper DirectVoxGO++: Fast Neural Radiance Fields for Object Reconstruction

License

Notifications You must be signed in to change notification settings

danperazzo/dvgoplusplus

Repository files navigation

DirectVoxGO++

Here is the code for our paper on DirectVoxGO++

Installation

Use the docker image

sudo docker build -t dvgopp .
sudo nvidia-docker run -it --rm --volume /:/host --workdir /host$PWD dvgopp

GO

To download the dataset, go to https://drive.google.com/file/d/1cCU35cIur-PXPKRqQxD6aKHWQmuEanoN/view?usp=share_link

Reproduction

All config files to reproduce our results:

$ ls configs/*
configs/llff:
africa.py  ship.py  basket.py torch.py  

Acknowledgement

The code base is origined from DirectVoxGO, we thank the authors for releasing their awesome code! We are standing on the shoulders of giants!:)

Citation

If you find this work helps you, please cite:

@article{perazzo2023directvoxgo++,
  title={DirectVoxGO++: Grid-based fast object reconstruction using radiance fields},
  author={Perazzo, Daniel and Lima, Jo{\~a}o Paulo and Velho, Luiz and Teichrieb, Veronica},
  journal={Computers \& Graphics},
  year={2023},
  publisher={Elsevier}
}

About

This repository is home to the code for the paper DirectVoxGO++: Fast Neural Radiance Fields for Object Reconstruction

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages