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NeuRIS (ECCV 2022)

We propose a new method, dubbed NeuRIS, for high quality reconstruction of indoor scenes.

Usage

Data preparation

Scene data used in NeuRIS can be downloaded from here and extract the scene data into folder dataset/indoor. And the scene data used in ManhattanSDF are also included for convenient comparisons. The data is organized as follows:

<scene_name>
|-- cameras_sphere.npz   # camera parameters
|-- image
    |-- 0000.png        # target image for each view
    |-- 0001.png
    ...
|-- depth
    |-- 0000.png        # target depth for each view
    |-- 0001.png
    ...
|-- pose
    |-- 0000.txt        # camera pose for each view
    |-- 0001.txt
    ...
|-- pred_normal
    |-- 0000.npz        # predicted normal for each view
    |-- 0001.npz
    ...
|-- xxx.ply		# GT mesh or point cloud from MVS
|-- trans_n2w.txt       # transformation matrix from normalized coordinates to world coordinates

Refer to the file for more details about data preparation of ScanNet or private data.

Setup

conda create -n neuris python=3.8
conda activate neuris
conda install pytorch=1.9.0 torchvision torchaudio cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

Training

python ./exp_runner.py --mode train --conf ./confs/neuris.conf --gpu 0 --scene_name scene0625_00

Mesh extraction

python exp_runner.py --mode validate_mesh --conf <config_file> --is_continue

Evaluation

python ./exp_evaluation.py --mode eval_3D_mesh_metrics

Citation

Cite as below if you find this repository is helpful to your project:

@inproceedings{wang2022neuris,
  title={Neuris: Neural reconstruction of indoor scenes using normal priors},
  author={Wang, Jiepeng and Wang, Peng and Long, Xiaoxiao and Theobalt, Christian and Komura, Taku and Liu, Lingjie and Wang, Wenping},
  booktitle={European Conference on Computer Vision},
  pages={139--155},
  year={2022},
  organization={Springer}
}

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