mip-NeRF

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF implementation. Contact Jon Barron if you encounter any issues.

Abstract

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call "mip-NeRF" (à la "mipmap"), extends NeRF to represent the scene at a continuously-valued scale. By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRF's ability to represent fine details, while also being 7% faster than NeRF and half the size. Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.

Installation

We recommend using Anaconda to set up the environment. Run the following commands:

# Clone the repo
git clone https://github.com/google/mipnerf.git; cd mipnerf
# Create a conda environment, note you can use python 3.6-3.8 as
# one of the dependencies (TensorFlow) hasn't supported python 3.9 yet.
conda create --name mipnerf python=3.6.13; conda activate mipnerf
# Prepare pip
conda install pip; pip install --upgrade pip
# Install requirements
pip install -r requirements.txt

[Optional] Install GPU and TPU support for Jax

# Remember to change cuda101 to your CUDA version, e.g. cuda110 for CUDA 11.0.
pip install --upgrade jax jaxlib==0.1.65+cuda101 -f https://storage.googleapis.com/jax-releases/jax_releases.html

Data

Then, you'll need to download the datasets from the NeRF official Google Drive. Please download and unzip nerf_synthetic.zip and nerf_llff_data.zip.

Generate multiscale dataset

You can generate the multiscale dataset used in the paper by running the following command,

python scripts/convert_blender_data.py --blenderdir /nerf_synthetic --outdir /multiscale

Running

Example scripts for training mip-NeRF on individual scenes from the three datasets used in the paper can be found in scripts/. You'll need to change the paths to point to wherever the datasets are located. Gin configuration files for our model and some ablations can be found in configs/. An example script for evaluating on the test set of each scene can be found in scripts/, after which you can use scripts/summarize.ipynb to produce error metrics across all scenes in the same format as was used in tables in the paper.

OOM errors

You may need to reduce the batch size to avoid out of memory errors. For example the model can be run on a NVIDIA 3080 (10Gb) using the following flag.

--gin_param="Config.batch_size = 1024"

Citation

If you use this software package, please cite our paper:

@misc{barron2021mipnerf,
      title={Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields},
      author={Jonathan T. Barron and Ben Mildenhall and Matthew Tancik and Peter Hedman and Ricardo Martin-Brualla and Pratul P. Srinivasan},
      year={2021},
      eprint={2103.13415},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgements

Thanks to Boyang Deng for JaxNeRF.