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https://github.com/snap-research/NeROIC
https://github.com/snap-research/NeROIC
Last synced: 24 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/snap-research/NeROIC
- Owner: snap-research
- License: other
- Created: 2022-01-07T21:28:21.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-01-19T22:21:36.000Z (almost 2 years ago)
- Last Synced: 2024-11-22T22:03:30.785Z (29 days ago)
- Language: Python
- Size: 8.5 MB
- Stars: 963
- Watchers: 89
- Forks: 124
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- StarryDivineSky - snap-research/NeROIC
README
# NeROIC: Neural Object Capture and Rendering from Online Image Collections
This repository is the official implementation of the NeROIC model from [NeROIC: Neural Object Capture and Rendering from Online Image Collections](https://arxiv.org/abs/2201.02533) by [Zhengfei Kuang](https://zhengfeikuang.com), [Kyle Olszewski](https://kyleolsz.github.io/), [Menglei Chai](https://mlchai.com/), [Zeng Huang](https://zeng.science/), [Panos Achlioptas](https://optas.github.io/), and [Sergey Tulyakov](http://stulyakov.com).
This work has been tested on Ubuntu 20.04.
Our project page is https://zfkuang.github.io/NeROIC.
## Quick Start
* Install required libraries.
```sh
conda env create -f environment.yaml
conda activate neroic
apt-get install graphicsmagick-imagemagick-compat
```
* Download our example data at [here](https://drive.google.com/drive/folders/1HzxaO9CcQOcUOp32xexVYFtsyKKULR7T?usp=sharing). (figure_dataset, milkbox_dataset and television_dataset)
* Optimize the geometry network.
```bash
python train.py \
--config \
--datadir
```
for example:
```bash
python train.py \
--config configs/milkbox_geometry.yaml \
--datadir ./data/milkbox_dataset
```
(Optional) running the script with multiple GPU is a bit tricky: you should first run it a single GPU (which will generate the resized images), then stop the process before the training starts. Then run:
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
--config \
--datadir \
--num_gpus 4
```
* Extract the normal from learned geometry.
```bash
python generate_normal.py \
--config \
--ft_path \
--datadir
```
for example:
```bash
python generate_normal.py \
--config configs/milkbox_geometry.yaml \
--ft_path logs/milkbox_geometry/epoch=29.ckpt \
--datadir data/milkbox_dataset
```
* Optimize the rendering network.
```bash
python train.py \
--config \
--ft_path \
--datadir
```
for example (training with 4 GPUs):
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
--config configs/milkbox_rendering.yaml \
--ft_path logs/milkbox_geometry/epoch=29.ckpt \
--datadir data/milkbox_dataset \
--num_gpus 4
```
training logs & results will be saved at `logs/`.
## Video Generation
the video of novel testing views is generated on-the-fly during training, and saved under `logs/`. To render video with a pre-trained model, run:
```bash
python train.py \
--config \
--ft_path \
--datadir \
--i_video 1 \
(optional) --test_img_id
```
## Testing
We provide additional testing scripts for material decomposition and relighting.
To decompose materials:
```bash
python test_material.py \
--config \
--ft_path \
--datadir
```
To relight the model (panoramic exr HDRI maps are prefered):
```bash
python test_relighting.py \
--config \
--ft_path \
--datadir \
--test_env_filename
```
## Bring Your Own Data
To train NeROIC with your own collected data, click [here](scripts/README.md).
## Model Overview
Our two-stage model takes images of an object from different conditions as input.
With the camera poses of images and object foreground masks acquired by other state-of-the-art methods,
We first optimize the geometry of scanned object and refine camera poses by training a NeRF-based network;
We then compute the surface normal from the geometry (represented by density function) using our normal extraction layer;
Finally, our second stage model decomposes the material properties of the object and solves for the lighting conditions for each image.
## Novel View Synthesis
Given online images from a common object, our model can synthesize novel views of the object with the lighting conditions from the training images.
https://user-images.githubusercontent.com/8952528/148708746-14c2db49-2516-4e31-a1be-559be4480b01.mp4
## Material Decomposition
https://user-images.githubusercontent.com/8952528/148708751-7fb1c820-57d3-454f-a411-635c141fab18.mp4
## Relighting
https://user-images.githubusercontent.com/8952528/148708757-f7c981f0-3963-49cb-a492-4fae73429105.mp4
## Citation
If you find this useful, please cite the following:
```bibtex
@article{10.1145/3528223.3530177,
author = {Kuang, Zhengfei and Olszewski, Kyle and Chai, Menglei and Huang, Zeng and Achlioptas, Panos and Tulyakov, Sergey},
title = {NeROIC: Neural Rendering of Objects from Online Image Collections},
year = {2022},
issue_date = {July 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {41},
number = {4},
issn = {0730-0301},
url = {https://doi.org/10.1145/3528223.3530177},
doi = {10.1145/3528223.3530177},
journal = {ACM Trans. Graph.},
month = {jul},
articleno = {56},
numpages = {12},
keywords = {neural rendering, reflectance & shading models, multi-view & 3D}
}
```
Acknowledgements: This work borrows many code from [NeRF-pl](https://github.com/kwea123/nerf_pl). We thank the author for releasing his code.