Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering
Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, Zhaopeng Cui.
ICCV 2021

We have tested the code on pytorch 1.8.1, while a newer version of pytorch should also work.

conda create -n object_nerf python=3.8
conda activate object_nerf
conda install pytorch==1.8.1 torchvision cudatoolkit=11.1 -c pytorch -c conda-forge
pip install -r requirements.txt

Please go to the data preparation.

You can run train.py to train the model, and here are two examples.

# train on ScanNet 0113
python train.py dataset_config=config/scannet_base_0113_multi.yml "img_wh=[640,480]" exp_name=my_expr_scannet_0113

# train on ToyDesk 2
python train.py dataset_config=config/toy_desk_2.yml "img_wh=[640,480]" exp_name=my_expr_toydesk_2

Here we provide two examples of scene editing with pre-trained models (download link).

python test/demo_editable_render.py \
    config=test/config/edit_scannet_0113.yaml \
    ckpt_path=../object_nerf_edit_demo_models/scannet_0113/last.ckpt \
    prefix=scannet_0113_duplicating_moving

python test/demo_editable_render.py \
    config=test/config/edit_toy_desk_2.yaml \
    ckpt_path=../object_nerf_edit_demo_models/toydesk_2/last.ckpt \
    prefix=toy_desk2_rotating

Remember to change the ckpt_path to the uncompressed model checkpoint file.

You can find the rendered image in debug/rendered_view/render_xxxxxx_scannet_0113_duplicating_moving or debug/rendered_view/render_xxxxxx_toy_desk2_rotating which should look as follows:

    

If you find this work useful, please consider citing:

@inproceedings{yang2021objectnerf,
    title={Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering},
    author={Yang, Bangbang and Zhang, Yinda and Xu, Yinghao and Li, Yijin and Zhou, Han and Bao, Hujun and Zhang, Guofeng and Cui, Zhaopeng},
    booktitle = {International Conference on Computer Vision ({ICCV})},
    month = {October},
    year = {2021},
}

In this project we use (parts of) the implementations of the following works:

• nerf_pl by kwea123.
• nerf_pytorch by Yen-Chen Lin.
• scannet by Angela Dai.

We thank the respective authors for open sourcing their methods.