Implementation of paper "NeRF-SOS: Any-View Self-supervised Object Segmentation from Complex Real-World Scenes " with PyTorch library.

Project Page | Paper

We recommend users to use conda to install the running environment. The following dependencies are required:

pytorch=1.7.0
torchvision=0.8.0
cudatoolkit=11.0
tensorboard=2.7.0
opencv
imageio
imageio-ffmpeg
configargparse
scipy
matplotlib
tqdm
mrc
lpips

To run our code on NeRF dataset, users can either download our combined (RGB+poses+masks) datasets through cloud drive and then generate .npy training data following the subsequent suggestions, or download original NeRF data from official cloud drive and official CO3Dv2 datasets. Then extract package files according to the following directory structure:

├── configs
│   ├── ...
│
├── datasets
│   ├── nerf_llff_data
│   │   └── flower 
│   │   └── fortress 
|   |   └── ...
│   ├── co3d_colmap
│   │   └── co3d_apple_110_13051_23361 
│   │   └── co3d_backpack_537_78249_152122  
|   |   └── ...

Then, please put the ``segments'' folder LINK under DATASET/SCENE/ (e.g., nerf_llff_data/flower/segments)

run the following command to generate training and testing data

cd data
python gen_dataset.py --config ../configs/flower_full.txt  --data_path /PATH_TO_DATA/nerf_llff_data_fullres/flower/  --data_type llff

We provide a data sample for scene ``Flower'' (with multi-view images, camera poses, masks and the processed .npy training files) can be found in the LINK, you can direct download it without any modification.

If you wanna see the rendered masks using the self-supervised trained model, run:

bash scrits/eval.sh

If you wanna see the rendered masks (video format) using the self-supervised trained model, run:

bash scrits/eval_video.sh

After preparing datasets, users can train a NeRF-SOS by the following command:

bash scripts/train_flower_node0.sh
bash scripts/train_fortress_node0.sh
bash scripts/train_co3d_apple_node0.sh
bash scripts/train_co3d_backpack_node0.sh

For more options, please check via the following instruction:

python run_nerf.py -h

While training, users can view logging information through tensorboard:

tensorboard --logdir='./logs' --port <your_port> --host 0.0.0.0

When training is done, users can synthesize exhibition video by running:

bash scrits/eval.sh

support COLMAP poses

If you find this repo is helpful, please cite:

@article{fan2022nerf,
  title={NeRF-SOS: Any-View Self-supervised Object Segmentation from Complex Real-World Scenes},
  author={Fan, Zhiwen and Wang, Peihao and Gong, Xinyu and Jiang, Yifan and Xu, Dejia and Wang, Zhangyang},
  journal={arXiv e-prints},
  pages={arXiv--2209},
  year={2022}
}