GARF : Gaussian Activated Radiance Fields for High Fidelity Reconstruction & Pose Estimation

Project Page | arXiv preprint | Paper | Colab Notebook

Shin-Fang Chng ¹, Sameera Ramasinghe ², Jamie Sherrah ¹, Simon Lucey ¹.

¹ Australian Institute for Machine Learning (AIML), University of Adelaide, ² Amazon, Australia

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Overview

We provide the PyTorch implementation for training NeRF (Gaussian-based) and GARF models, along with a Colab demo for an image fitting task.

🕵️ Google Colab

If you want to explore Gaussian activation, please check out our Colab notebook which allows you to experiment it with a neural image representation task.

🛠️ Installation Steps

Assuming a fresh Anaconda environment, you can install the dependencies by

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

💿 Training data

1. LLFF

You can download the real-world dataset by running

gdown 16VnMcF1KJYxN9QId6TClMsZRahHNMW5g
rm -f nerf_llff_data.zip
mv nerf_llff_data data/llff

2. BLEFF

You can download the synthetic forward-facing dataset by running

wget https://www.robots.ox.ac.uk/~ryan/nerfmm2021/BLEFF.tar.gz
tar -xzvf BLEFF.tar.gz
mv BLEFF data/bleff

The data directory should contain the subdirectories llff and bleff. If you have downloaded the datasets, you can create soft links to them within the data directory.

⏳ Training

By default, models and TensorBoard event files are saved to ~/output/<GROUP>/<NAME>. This can be modified using the --output_root flag.

Full MLP training (GARF):

To optimize GARF from scratch by initializing camera poses with identity

1. For LLFF dataset

python train.py --model=garf --yaml=garf_llff --group=<GROUP> --name=<NAME> --data.dataset=llff --data.scene="fern" --optim.sched=!

2. For BLEFF dataset

python train.py --model=garf --yaml=garf_bleff --group=<GROUP> --name=<NAME> --data.dataset=bleff --data.scene="balls1" --data.mode="mix_rt/t000r000" --optim.sched=!

To optimize GARF from Colmap estimation, by initializing camera poses with estimation from Colmap

1. For LLFF dataset

python train.py --model=garf --yaml=garf_llff --group=<GROUP> --name=<NAME> --data.dataset=llff --data.scene="fern" --optim.sched=! --init.pose=True --init.pose_warmup=2000

2. For BLEFF dataset

python train.py --model=garf --yaml=garf_bleff --group=<GROUP> --name=<NAME> --data.dataset=bleff --data.scene="balls1" --data.mode="mix_rt/t000r000" --optim.sched=! --init.pose=True --init.pose_warmup=2000

Spherical Harmonics-based training (GARF-SH):

python train.py --model=garf_sh --yaml=garf_sh_llff --group=<GROUP> --name=<NAME> --data.dataset=llff --data.scene="fern" --optim.sched=!

Gaussian-activated NeRF training:

python train.py --model=nerf_gaussian --yaml=nerf_gaussian_llff --group=<GROUP> --name=<NAME> --data.dataset=llff --data.scene="fern" --optim.sched=!

🔎 Evaluation

This code evaluates poses, image quality metrics (PSNR/LPIPS/SSIM) of the test set, and rendering novel views. By default, if the value is not provided for resume=<NUM_ITER>, it will automatically load the most recent checkpoint.

python evaluate.py --model=garf --yaml=garf_llff --group=<GROUP> --name=<NAME> --data.dataset=llff --data.scene="fern" --optim.sched=! --resume=<NUM_ITER>

🙇 Special Thanks

This codebase heavily drawns upon the amazing codebase of BARF: Bundle Adjusting Neural Radiance Fields. We thank Chen-Hsuan Lin, Huangying Zhan and Tonghe for their insightful discussions.

👩‍💻 Citation

This code is for non-commercial use. If you find our work useful in your research please consider citing our paper:

@inproceedings{chng2022gaussian,
  title         = {Gaussian activated neural radiance fields for high fidelity reconstruction and pose estimation},
  author        = {Chng, Shin-Fang and Ramasinghe, Sameera and Sherrah, Jamie and Lucey, Simon},
  booktitle     = {The European Conference on Computer Vision: ECCV},
  year          = {2022}
}

@inproceedings{ramasinghe2022beyond,
  title         = {Beyond periodicity: towards a unifying framework for activations in coordinate-MLPs},
  author        = {Ramasinghe, Sameera and Lucey, Simon},
  booktitle     = {The European Conference on Computer Vision: ECCV},
  year          = {2022}
}