https://cseweb.ucsd.edu/~viscomp/projects/NeuMIP
The code was developed on Ubuntu 18.04, using Python 3.7 and PyTorch 1.7. Although, we don't use any unique features of those systems, running on different configuration might require some small adjustments.
First you need to setup your data path, where all the datasets, models, etc. reside. Go to path_config.py and at line 9 change:
path_base = "$YOUR_PATH to your path.
The code is licensed under BSD license.
Here's the link to the datasets and models: https://drive.google.com/drive/folders/1EbzMlpmE7f49jTs2IY7oMQKZAGAfl3M5
To train:
./rung.sh 0 ./neural_rendering.py --dataset DATASET_NAME \
--max_iter 30000 --outm MODEL_NAME \
--experiment StandardRawLongShadowMaskOnly \
--batch 4 --loss comb2
To visualize it:
./run.sh neural_rendering.py --inm wool2.pth --vis
The main code is in the neural_rendering.py file. Beware, the code supports different configurations/architectures. Please, use -experiment StandardRawLongShadowMaskOnly For loss, use l1 or comb2, which is better for specular.