https://github.com/google/hyperNeRF

Code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".
https://github.com/google/hyperNeRF

3d machine-learning nerf neural-network neural-rendering novel-view-synthesis

Last synced: 5 days ago
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Code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".

• Host: GitHub
• URL: https://github.com/google/hyperNeRF
• Owner: google
• License: apache-2.0
• Created: 2021-09-09T05:40:25.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-05-03T20:24:40.000Z (6 months ago)
• Last Synced: 2024-11-07T03:31:47.148Z (5 days ago)
• Topics: 3d, machine-learning, nerf, neural-network, neural-rendering, novel-view-synthesis
• Language: Python
• Homepage: https://hypernerf.github.io
• Size: 89.8 KB
• Stars: 893
• Watchers: 22
• Forks: 106
• Open Issues: 34
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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• awesome-dynamic-NeRF - [Code

README

        
# HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

This is the code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".

* [Project Page](https://hypernerf.github.io)

* [Paper](https://arxiv.org/abs/2106.13228)

* [Video](https://www.youtube.com/watch?v=qzgdE_ghkaI)

This codebase implements HyperNeRF using [JAX](https://github.com/google/jax),

building on [JaxNeRF](https://github.com/google-research/google-research/tree/master/jaxnerf).

## Demo

We provide an easy-to-get-started demo using Google Colab!

These Colabs will allow you to train a basic version of our method using

Cloud TPUs (or GPUs) on Google Colab.

Note that due to limited compute resources available, these are not the fully

featured models and will train quite slowly and the quality will likely not be that great.

If you would like to train a fully featured model, please refer to the instructions below

on how to train on your own machine.

| Description      | Link |

| ----------- | ----------- |

| Process a video into a dataset| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/nerfies/blob/main/notebooks/Nerfies_Capture_Processing.ipynb)|

| Train HyperNeRF| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/hypernerf/blob/main/notebooks/HyperNeRF_Training.ipynb)|

| Render HyperNeRF Videos| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/hypernerf/blob/main/notebooks/HyperNeRF_Render_Video.ipynb)|

## Setup

The code can be run under any environment with Python 3.8 and above.

(It may run with lower versions, but we have not tested it).

We recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and setting up an environment:

    conda create --name hypernerf python=3.8

Next, install the required packages:

    pip install -r requirements.txt

Install the appropriate JAX distribution for your environment by  [following the instructions here](https://github.com/google/jax#installation). For example:

    # For CUDA version 11.1

    pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html

## Training

After preparing a dataset, you can train a Nerfie by running:

    export DATASET_PATH=/path/to/dataset

    export EXPERIMENT_PATH=/path/to/save/experiment/to

    python train.py \

        --base_folder $EXPERIMENT_PATH \

        --gin_bindings="data_dir='$DATASET_PATH'" \

        --gin_configs configs/test_local.gin

To plot telemetry to Tensorboard and render checkpoints on the fly, also

launch an evaluation job by running:

    python eval.py \

        --base_folder $EXPERIMENT_PATH \

        --gin_bindings="data_dir='$DATASET_PATH'" \

        --gin_configs configs/test_local.gin

The two jobs should use a mutually exclusive set of GPUs. This division allows the

training job to run without having to stop for evaluation.

## Configuration

* We use [Gin](https://github.com/google/gin-config) for configuration.

* We provide a couple preset configurations.

* Please refer to `config.py` for documentation on what each configuration does.

* Preset configs:

    - `hypernerf_vrig_ds_2d.gin`: The deformable surface configuration for the validation rig (novel-view synthesis) experiments.

    - `hypernerf_vrig_ap_2d.gin`: The axis-aligned plane configuration for the validation rig (novel-view synthesis) experiments.

    - `hypernerf_interp_ds_2d.gin`: The deformable surface configuration for the interpolation experiments.

    - `hypernerf_interp_ap_2d.gin`: The axis-aligned plane configuration for the interpolation experiments.

## Dataset

The dataset uses the [same format as Nerfies](https://github.com/google/nerfies#datasets).

## Citing

If you find our work useful, please consider citing:

```BibTeX

@article{park2021hypernerf,

  author = {Park, Keunhong and Sinha, Utkarsh and Hedman, Peter and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Martin-Brualla, Ricardo and Seitz, Steven M.},

  title = {HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields},

  journal = {ACM Trans. Graph.},

  issue_date = {December 2021},

  publisher = {ACM},

  volume = {40},

  number = {6},

  month = {dec},

  year = {2021},

  articleno = {238},

}

```