This release contains code for predicting incident illumination at any 3D location within a scene. The algorithm takes a narrow-baseline stereo pair of RGB images as input, and predicts a multiscale RGBA lighting volume. Spatially-varying lighting within the volume can then be computed by standard volume rendering.

interiornet_test.py contains an example script for running a pretrained model on the test set (formatted as .npz files). Please download and extract the pretrained model and testing examples files, and then include the corresponding file/directory names as command line flags when running interiornet_test.py.

Example usage (edit paths to match your directory structure): python -m lighthouse.interiornet_test --checkpoint_dir="lighthouse/model/" --data_dir="lighthouse/testset/" --output_dir="lighthouse/output/"

Please refer to the train.py for code to use for training your own model.

This model was trained using the InteriorNet dataset. It may be helpful to read data_loader.py to get an idea of how we organized the InteriorNet dataset for training.

To train with the perceptual loss based on VGG features (as done in the paper), please download the imagenet-vgg-verydeep-19.mat pretrained VGG model, and include the corresponding path as a command line flag when running train.py.

Example usage (edit paths to match your directory structure): python -m lighthouse.train --vgg_model_file="lighthouse/model/imagenet-vgg-verydeep-19.mat" --load_dir="" --data_dir="lighthouse/data/InteriorNet/" --experiment_dir=lighthouse/training/

This model is quite memory-hungry, and we used a NVIDIA Tesla V100 GPU for training and testing with a single example per minibatch. You may run into memory constraints when training on a GPU with less than 16 GB memory or testing on a GPU with less than 12 GB memory. If you wish to train a model on a GPU with <16 GB memory, you may want to try removing the finest volume in the multiscale representation (see the model parameters in train.py).

If you find this code helpful, please cite our paper: @article{Srinivasan2020, author = {Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely}, title = {Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination}, journal = {CVPR}, year = {2020}, }