This project is a PyTorch implementation of Neural RGB-D Surface Reconstruction, which is a novel approach for 3D reconstruction that combines implicit surface representations with neural radiance fields
git clone https://github.com/HengyiWang/neural-rgbd-torch
cd neural-rgbd-torch
pip install -r requirements.txt
Please also install the external Marching cube packages via:
cd external/NumpyMarchingCubes
python setup.py install
You can also try the google colab notebook neural-rgbd-torch.ipynb
The ScanNet dataset can be downloaded via the following link neural_rgbd_data. The ICL data can be downloaded from the original author's webpage
python optimize.py --config configs/<config_file>.txt
Thanks for the author for their amazing works:
@InProceedings{Azinovic_2022_CVPR,
author = {Azinovi\'c, Dejan and Martin-Brualla, Ricardo and Goldman, Dan B and Nie{\ss}ner, Matthias and Thies, Justus},
title = {Neural RGB-D Surface Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {6290-6301}
}