Tensorflow implementation of optimizing a neural representation for a single scene and rendering new views.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall*¹, Pratul P. Srinivasan*¹, Matthew Tancik*¹, Jonathan T. Barron², Ravi Ramamoorthi³, Ren Ng^1
1UC Berkeley, ²Google Research, ³UC San Diego
*denotes equal contribution

Python 3 dependencies:

• Tensorflow 1.15
• matplotlib
• numpy
• imageio
• configargparse

The LLFF data loader requires ImageMagick.

You will also need the LLFF code (and COLMAP) set up to compute poses if you want to run on your own real data.

Run

bash download_example_data.sh

to get the our synthetic Lego dataset and the LLFF Fern dataset. To optimize a low-res Fern NeRF:

python run_nerf.py --config config_fern.txt

To optimize a low-res Lego NeRF:

python run_nerf.py --config config_lego.txt

Run

bash download_example_weights.sh

to get a pretrained high-res NeRF for the Fern dataset. Now you can use the render_demo.ipynb to render new views.