More details can be found at the project page here.

A conda virtual environment is recommended.

pip install -r requirements.txt

Dataset dir should contain a folder named nerf_synthetic with various datasets in the blender configuration.

python train.py -m expname=v38_noupsample model=microfacet_tensorf2 dataset=ficus,drums,ship,teapot vis_every=5000 datadir={dataset dir}

Experiment configurations are done using hydra, which controls the initialization parameters for all of the modules. Look in configs/model to see what options are available. Setting the BRDF activation would look like adding this:

model.arch.model.brdf.activation="sigmoid"

to the command line argument.

To relight a dataset, you need to first convert the environment map .exr file to a pytorch checkpoint {envmap}.th like this:

python -m scripts.pano2cube backgrounds/christmas_photo_studio_04_4k.exr --output backgrounds/christmas.th

Then, after training some model and obtaining a checkpoint {ckpt}.th, you can run

python train.py -m expname=v38_noupsample model=microfacet_tensorf2 dataset=ficus vis_every=5000 datadir={dataset dir} ckpt={ckpt}.th render_only=True fixed_bg={envmap}.th

Note that something is currently wrong with computation of metrics in the current code and the scripts reval_lpips.ipynb and reeval_norm_err.ipynb currently have to be run. tabularize.ipynb can be used to create the tables, while other fun visualizations are available. You can also download our relighting experiments from here.

Other dataset configurations are available in configs/dataset. Real world datasets are available and do work.

Here is a link to the relighting dataset.