HDR-NeRF: High Dynamic Range Neural Radiance Fields

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We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures.

Method Overview

The pipeline of HDR-NeRF modeling the simplified physical process. Our method is consisted of two modules: an HDR radiance field models the scene for radiance and densities and a tone mapper models the CRF for colors.

Quick Start

Setup

git clone https://github.com/shsf0817/hdr-nerf.git
cd hdr-nerf
pip install -r requirements.txt

Dependencies (click to expand)

torch==1.9.0
torchvision==0.10.0
numpy==1.19.0
imageio-ffmpeg==0.4.5
imageio==2.9.0
opencv-python==4.5.3.56
tqdm==4.62.2
scikit-image==0.17.2
ConfigArgParse==1.5.2

We provide a pip environment setup file including all of the above dependencies. To read and write EXR files, run following commands at Python terminal:

    import imageio
    imageio.plugins.freeimage.download()

Download dataset

We collect an HDR dataset (multi-view and multi-exposure) that contains 8 synthetic scenes rendered with Blender and 4 real scenes captured by a digital camera. Images are collected at 35 different poses in the real dataset, with 5 different exposure time ${t_1, t_2, t_3, t_4, t_5}$ at each pose. You can download all our dataset in here.

For a quick demo, please download demo folder and move it to hdr-nerf folder.

Render a demo

python3 run_nerf.py --config configs/demo.txt --render_only

Both LDR and HDR results are saved in <basedir>/<expname>_<render_out_path> . All HDR results in the experiment are tonemapped using Phototmatix. Please install Phototmatix or Luminance HDR for the visualization of HDR results.

Train HDR-NeRF

python3 run_nerf.py --config configs/flower.txt

Intermediate results and models are saved in <basedir>/<expname>

Cite

@article{huang2021hdr,
  title={HDR-NeRF: High Dynamic Range Neural Radiance Fields},
  author={Huang, Xin and Zhang, Qi and Ying, Feng and Li, Hongdong and Wang, Xuan and Wang, Qing},
  journal={arXiv preprint arXiv:2111.14451},
  year={2021}
}

Acknowledge

Our code is based on the famous pytorch reimplementation of NeRF, nerf-pytorch. We appreciate all the contributors.