rho-nlr

Neural Lumigraph Rendering with proof-of-concept controllable illumination in TensorFlow 2.
_{Undergraduate project by Laura Ann Perkins, New College of Florida.}

The idea behind rho-nlr is to have the appearance model output coefficients in a spherical harmonic basis. This allows for simultaneous novel view synthesis and relighting from a sparse model, given supervision of light direction.

COMING SOON: pretrained models and more details.

Requirements

• This project requires Python 3.6+ with TensorFlow 2.5.0 (among other dependencies).
• For the SIREN models, it builds upon tf_siren.

Usage

You can train a model from scratch with train.py.

python train.py --dataset_path path/to/data/folder --img_size max_dimension --out_folder path/to/output/folder

To evaluate a fitted model, run test.py.

python test.py --model path/to/fitted/model --dataset_path path/to/data/folder --img_size max_dimension

For more options, try running either script with the help flag, e.g. python train.py -h, or edit the config file.