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NeuRIS (ECCV 2022)

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

Project page | Paper | Data

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}
}