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NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

arXiv

Project Website | Paper | arXiv

This repository contains the code of the short paper NOVA accepted to the CV4Metaverse ICCV 2023 Workshop. If you find this paper and code useful for your research, please consider citing the following paper:

@InProceedings{Agrawal_2023_ICCV,
    author    = {Agrawal, Dakshit and Xu, Jiajie and Mustikovela, Siva Karthik and Gkioulekas, Ioannis and Shrivastava, Ashish and Chai, Yuning},
    title     = {NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2023},
    pages     = {4288-4292}
}

Contents

  1. Setup Instructions and Dependencies
  2. Dataset
  3. Train NOVA
  4. Render Samples
  5. Evaluation
  6. License
  7. Acknowledgements

1. Setup Instructions and Dependencies

The code is test with

  • Linux (tested on Ubuntu 18.04)
  • Miniconda 3
  • Python 3.9
  • Pytorch 2.0
  • CUDA 11.7
  • GPU with 24 GB VRAM

To get started, please create the conda environment nova by running

conda create --name nova python=3.9
conda activate nova
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

2. Dataset

The Dynamic Scene Dataset is used for our experiments. Please download the pre-processed data by running:

cd $ROOT_PATH
gdown https://drive.google.com/uc\?id\=14E6jIUVx_cuXPKlSefHo5tEtDMt7WPUd
unzip data.zip
rm data.zip

3. Train NOVA

You can train a model from scratch by running:

cd $ROOT_PATH/
python run_nerf.py --config configs/config_Balloon1.txt

4. Render Samples

You can render the results by running:

cd $ROOT_PATH/
python render_samples.py --config logs/Balloon1_H270_NOVA/config.txt

To render multiple objects in the scene, comment lines 202-203 and change lines 204-206 in render_samples.py to the following (only rotation supported for now):

# list of axis along which rotation occurs
axis = []
# rotation angle in degrees, can be negative, must have the same number of elements as axis
angle = []
# 0 specifies background, specify the object id corresponding to each element in axis
render_kwargs_test.update({"cam_order": [0, ...]})


# For example, if there are two objects in the scene, and you want one instance of first
# object and two instances of the second object, you can define something like this:
axis = ["x", "x", "y"]
angle = [0, -10, 15]
render_kwargs_test.update({"cam_order": [0, 1, 2, 2]})

We provide our trained models. You can download them by running:

cd $ROOT_PATH/
gdown https://drive.google.com/uc\?id\=1ZlF1uG4KG_7-ifY7qtClnELtz0DZ5KTn
unzip logs.zip
rm logs.zip

5. Evaluation

We quantitatively evaluate the fix-view-change-time results of the following methods:

NeRF + t
Yoon et al.
NSFF
DynamicNeRF
NOVA (ours)

Please download the results by running:

cd $ROOT_PATH/
gdown https://drive.google.com/uc\?id\=1y0RvV4jzkqcEdOOUHR_7hAGbLWPPIki3
unzip results.zip
rm results.zip

Then you can calculate the PSNR/SSIM/LPIPS by running:

cd $ROOT_PATH
python utils/evaluation.py

The NOVA (our) results above differ slightly from the results shown in the paper because the models have been retrained. The PSNR metrics of the provided trained models are as follows:

  1. Balloon1 -- 21.51
  2. Balloon2 -- 23.74
  3. Jumping -- 19.88
  4. Playground -- 22.70
  5. Skating -- 26.38
  6. Truck -- 23.34
  7. Umbrella -- 23.10

Average -- 22.95

To download the results folder that corresponds to the PSNR metrics quoted in the paper, please download the following:

cd $ROOT_PATH/
gdown https://drive.google.com/uc\?id\=1M4yJ66n-VqJoR0r-7PSklGjlcSUI1xCN
unzip results_paper.zip
rm results_paper.zip

6. License

This work is licensed under MIT License. See LICENSE for details.

7. Acknowledgements

Our training code is build upon DynamicNeRF.