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‎CONTRIBUTING.md‎

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# How to Contribute
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We'd love to accept your patches and contributions to this project. There are
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just a few small guidelines you need to follow.
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You generally only need to submit a CLA once, so if you've already submitted one
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## Community Guidelines
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This project follows
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[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
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‎LICENSE‎

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‎README.md‎

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# HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
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This is the code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".
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* [Project Page](https://hypernerf.github.io)
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* [Paper](https://arxiv.org/abs/2106.13228)
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* [Video](https://www.youtube.com/watch?v=qzgdE_ghkaI)
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This codebase contains a re-implementation of Nerfies using [JAX](https://github.com/google/jax),
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building on [JaxNeRF](https://github.com/google-research/google-research/tree/master/jaxnerf).
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We have been careful to match implementation details and have reproduced the original
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results presented in the paper.
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## Setup
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The code can be run under any environment with Python 3.8 and above.
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(It may run with lower versions, but we have not tested it).
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We recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and setting up an environment:
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conda create --name hypernerf python=3.8
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Next, install the required packages:
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pip install -r requirements.txt
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Install the appropriate JAX distribution for your environment by [following the instructions here](https://github.com/google/jax#installation). For example:
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# For CUDA version 11.1
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pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
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## Training
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After preparing a dataset, you can train a Nerfie by running:
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export DATASET_PATH=/path/to/dataset
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export EXPERIMENT_PATH=/path/to/save/experiment/to
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python train.py \
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--base_folder $EXPERIMENT_PATH \
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--gin_bindings="data_dir='$DATASET_PATH'" \
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--gin_configs configs/test_local.gin
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To plot telemetry to Tensorboard and render checkpoints on the fly, also
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launch an evaluation job by running:
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python eval.py \
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--base_folder $EXPERIMENT_PATH \
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--gin_bindings="data_dir='$DATASET_PATH'" \
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--gin_configs configs/test_local.gin
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The two jobs should use a mutually exclusive set of GPUs. This division allows the
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training job to run without having to stop for evaluation.
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## Configuration
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* We use [Gin](https://github.com/google/gin-config) for configuration.
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* We provide a couple preset configurations.
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* Please refer to `config.py` for documentation on what each configuration does.
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* Preset configs:
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- `hypernerf_vrig_ds_2d.gin`: The deformable surface configuration for the validation rig (novel-view synthesis) experiments.
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- `hypernerf_vrig_ap_2d.gin`: The axis-aligned plane configuration for the validation rig (novel-view synthesis) experiments.
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- `hypernerf_interp_ds_2d.gin`: The deformable surface configuration for the interpolation experiments.
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- `hypernerf_interp_ap_2d.gin`: The axis-aligned plane configuration for the interpolation experiments.
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## Dataset
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The dataset uses the [same format as Nerfies](https://github.com/google/nerfies#datasets).
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## Citing
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If you find our work useful, please consider citing:
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```BibTeX
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@article{park2021hypernerf
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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.},
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title = {HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields},
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journal = {arXiv preprint arXiv:2106.13228},
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year = {2021},
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}
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```

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