https://jiepengwang.github.io/NeuRIS/

https://jiepengwang.github.io/NeuRIS/

Last synced: 27 days ago
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• Host: GitHub
• URL: https://jiepengwang.github.io/NeuRIS/
• Owner: jiepengwang
• License: mit
• Created: 2022-06-18T17:14:06.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-30T05:54:35.000Z (about 1 year ago)
• Last Synced: 2024-08-03T17:10:06.963Z (4 months ago)
• Language: Python
• Size: 34.7 MB
• Stars: 222
• Watchers: 10
• Forks: 16
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# NeuRIS (ECCV 2022)

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

![](./doc/teaser.png)

## [Project page](https://jiepengwang.github.io/NeuRIS/) |  [Paper](https://arxiv.org/abs/2206.13597) | [Data](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/jiepeng_connect_hku_hk/Er7bpbBAxMBBnZfDvdvrO1kBu2tkTpnMw9XXfeuQzkwOlA?e=Yf0Bbj)

## Usage

#### Data preparation

Scene data used in NeuRIS can be downloaded from [here](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/jiepeng_connect_hku_hk/ElKcK1sus9pLnARZ_e9l-IcBS6cE-6w8xt34bMsvMAiuIQ?e=0z1eka) and extract the scene data into folder `dataset/indoor`. And the scene data used in [ManhattanSDF](https://github.com/zju3dv/manhattan_sdf) are also included for convenient comparisons.

The data is organized as follows:

```

|-- 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](https://github.com/jiepengwang/NeuRIS/blob/main/preprocess/README.md) 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  --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}

}

```