GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields

SIGGRAPH Asia 2023

Environment Setup

Follow the nerfstudio instuctions upto and including the pytorch and tiny-cuda-nn installation. Then clone this repository and install:

git clone https://github.com/barbararoessle/ganerf.git
cd ganerf
python -m pip install --upgrade pip setuptools
python -m pip install -e .

Dataset

Download the ScanNet++ dataset following the instructions. It is sufficient to download only the DSLR data.

Preprocessing

We undistort and downsize the images:

python scripts/undistort_scannetpp.py \
--input_dir <directory to ScanNet++> \
--output_dir <directory for undistorted ScanNet++ scenes> \
--scene 2e74812d00

If no scene is specified, all scenes will be converted. We continue the commands in this README for the scene 2e74812d00.

Training Stage 1

The 1st training stage does a NeRF optimization with discriminator (paper section 3.2).

ns-train ganerf \
--output-dir <directory for writing results and checkpoints> \
--data <directory of undistorted ScanNet++ scenes>/2e74812d00 \
--pipeline.model.far-plane 4 \
scannetpp-data

As in nerfstudio, each experiment is logged into a subdirectory named with the starting time in the format jjjj-mm-dd_hhmmss, to which we refer as experiment name in the following.

Training Stage 2

The 2nd training stage trains a conditional generator (paper section 3.3). First, the generator training is prepared by rendering all train and test views with the checkpoint from stage 1. The renderings, together with the ground truth images, are written to <experiment name>/generator_dataset_<iteration> to form the dataset for generator training.

python scripts/prepare_generator_training.py \
--output_dir <directory for writing results and checkpoints> \
--scene 2e74812d00 \
--exp_name <experiment name>

Then, the generator training is started.

python scripts/train_generator.py \
--output_dir <directory for writing results and checkpoints> \
--scene 2e74812d00 \
--exp_name <experiment name>

Test renderings, metrics and checkpoints are written to <experiment name>/generator.

Video

Finally, a video can be rendered as follows:

ns-render camera-path \
--load-config <directory for writing results and checkpoints>/2e74812d00/ganerf/<experiment name>/config.yml \
--load-step 200000 \
--output_format images \
--camera-path-filename <path to ganerf repository clone>/assets/2e74812d00/30fps.json \
--output-path <directory for writing results and checkpoints>/2e74812d00/ganerf/<experiment name>/30fps.mp4 \
--run_generator True \
--load_original_intrinsics True \
--aspect_ratio_9_16 True

Citation

If you find this repository useful, please cite:

@article{roessle2023ganerf,
        title={GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields}, 
        author={Roessle, Barbara and M{\"u}ller, Norman and Porzi, Lorenzo and Bul{\`o}, Samuel Rota and Kontschieder, Peter and Nie{\ss}ner, Matthias},
        year = {2023},
        issue_date = {December 2023},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        volume = {42},
        number = {6},
        issn = {0730-0301},
        url = {https://doi.org/10.1145/3618402},
        doi = {10.1145/3618402},
        journal = {ACM Trans. Graph.},
        month = {nov},
        articleno = {207},
        numpages = {14},
}

Acknowledgements

We thank nerfstudio and stylegan2-ada-pytorch, from which this repository borrows code.

barbararoessle / ganerf