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https://jiepengwang.github.io/NeuRIS/
https://jiepengwang.github.io/NeuRIS/
Last synced: 3 months ago
JSON representation
- 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 (12 months ago)
- Last Synced: 2024-04-21T09:49:58.041Z (7 months ago)
- Language: Python
- Size: 34.7 MB
- Stars: 220
- Watchers: 10
- Forks: 16
- Open Issues: 10
-
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}
}
```