Merical / nerf-pytorch

Reimplementation of ECCV paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" with PyTorch Library.

Neural Radiance Field - PyTorch Implementation

Reimplementation of ECCV paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" with PyTorch library. Our code's key features include simplicity, reusability, high-level encapsulation, and extensive tunable hyper-parameters.

Installation

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

Data Preparation

To run our code on NeRF dataset, users need first download data from official cloud drive. Then extract package files according to the following directory structure:

├── configs
│   ├── ...
│
├── datasets
│   ├── nerf_llff_data
│   │   └── fern
│   │   └── flower  # downloaded llff dataset
│   │   └── horns   # downloaded llff dataset
|   |   └── ...
|   ├── nerf_synthetic
|   |   └── lego
|   |   └── ship    # downloaded synthetic dataset
|   |   └── ...

Training

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

python run_nerf.py --gpuid <gpu_id> --expname <output_folder> --config <default_config_file>

<default_config_file> is the path to the configuration file of your experiment instance. Examples and pre-defined configuration files are provided in configs folder. <output_folder> is the output folder name under logs/ directory.

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

Evaluation

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

python run_nerf.py --action video --gpuid <your_device> --expname <output_folder> --config <default_config_file>

To visualize geometry, users can first generate density field with flag --eval_vol:

python run_nerf.py --eval --eval_vol --gpuid <gpu_id> --config <default_config_file>

The exported volume will be saved into <expname>/eval/ directory (with both .npy and .mrc suffices). Then users can use Python library PyMCubes or USCF Chimera to threshold and extract meshes from the density field.

Acknowledgement

Our code is implemented based on the following repository.

@misc{lin2020nerfpytorch,
  title={NeRF-pytorch},
  author={Yen-Chen, Lin},
  howpublished={\url{https://github.com/yenchenlin/nerf-pytorch/}},
  year={2020}
}