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A PyTorch implementation of the CVPR 2022 Paper "Neural RGB-D Surface Reconstruction"

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HengyiWang/neural-rgbd-torch

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neural-rgbd-torch

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

Installation

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

Dataset

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

Run

python optimize.py --config configs/<config_file>.txt

Citation

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}
}

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A PyTorch implementation of the CVPR 2022 Paper "Neural RGB-D Surface Reconstruction"

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