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This repository contains the code for Learning Neural Light Fields with Ray-Space Embedding Networks.

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Learning Neural Light Fields with Ray-Space Embedding

This repository contains a pytorch-lightning implementation for the paper Learning Neural Light Fields with Ray-Space Embedding. The entirety of neural-light-fields is licensed under the MIT license. The design of this project was inspired by nerf_pl.

Table of contents



Installation

To set up an environment using conda, with all of the appropriate Python dependencies, run

conda env create -f environment.yml

Next, to build the NSVF voxel octree intersection package, run

python setup.py build_ext --inplace

Datasets

We have consolidated all of the datasets we make use of here. Note that we have made no modifications to the original datasets, they are simply collected here for convenience.

In order to use one of the zipped datasets, unzip it and place it within the data/ folder.

Training

Stanford Lightfield Training

To train a neural light field on the Stanford Light Field Dataset, run

python main.py experiment=stanford_lf experiment/dataset=stanford_<scene>

You can change the model / embedding network with

python main.py experiment=stanford_lf experiment/model=stanford_<affine/feature/no_embed>

Shiny Dense Lightfield Training

To train a neural light field on the Shiny Dataset (CD or Lab scenes), run

python main.py experiment=shiny_lf_dense experiment.dataset.collection=<scene>

LLFF Subdivided Lightfield Training

To train a subdivided neural light field on NeRF's Real Forward Facing Dataset, run

python main.py experiment=llff_subdivided experiment.dataset.collection=<scene>  experiment.model.subdivision.max_hits=<num_subdivisions>

Shiny Subdivided Lightfield Training

To train a subdivided neural light field on the Shiny dataset, run

python main.py experiment=shiny_subdivided experiment.dataset.collection=<scene> experiment.model.subdivision.max_hits=<num_subdivisions>

We use a slightly different configuration for the denser CD and Lab sequences from the Shiny dataset (larger batch size). For these sequences, run

python main.py experiment=shiny_subdivided_dense experiment.dataset.collection=<scene> experiment.model.subdivision.max_hits=<num_subdivisions>

Testing

Testing is performed automatically during training with frequency dictated by experiment.training.test_every. Results are written by default to logs/<experiment_name>/val_images/<epoch>. You can also manually trigger testing by running

python main.py ... <model_settings> ... experiment.test_only=True

In this case, the test set predictions and ground truth will be written out to <log_dir>/testset

Rendering

Rendering is performed automatically during training with frequency dictated by experiment.training.render_every. Individual video frames are written by default to logs/<experiment_name>/val_videos/<epoch>. You can also manually trigger rendering by running

python main.py ... <model_settings> ... experiment.render_only=True

In this case, the individual rendered frames will be written out to <log_dir>/render

Results

We provide all of the predicted images that we use to compute our evaluation metrics here.

Evaluation

You will need to create a new conda environment to run the evaluation code, since it relies on a different version of CUDA / tensorflow

cd baselines/third_party/evaluation
conda env create -f environment.yml

For structured light field predicitons, where your directory containing image predictions from the model has all images, including predictions on training images (e.g. from our modified X-Fields codebase), run

python run_evaluation.py --mode lightfield --gt_dir <gt_dir> --pred_dir <pred_dir> --out_dir <out_dir> --metrics_file <out_metrics_file>

When you have only heldout images, and ground truth / predictions are in the same directory (e.g. our method and NeRF), run

python run_evaluation.py --mode same_dir --gt_dir <gt_dir> --out_dir <out_dir> --metrics_file <out_metrics_file>

Citation

@inproceedings{attal2022learning,
  author    = {Benjamin Attal and Jia-Bin Huang and Michael Zollh{\"o}fer and Johannes Kopf and Changil Kim},
  title     = {Learning Neural Light Fields with Ray-Space Embedding Networks},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
}

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