This repo holds the model training code for the TVCG paper, "Interactive Isosurface Visualization in Memory Constrained Environments Using Deep Learning and Speculative Raycasting" by Landon Dyken, Will Usher, and Sidharth Kumar. The code here was adapted from the work of "FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks" for our training purposes.

Training and inference in this codebase require an NVIDIA GPU with CUDA support, along with conda for dependencies.

• To begin, 'install.sh' must first be run to install necessary dependencies.
• For example purposes, sample data is given here for training and inference in the 'data/' folder, split into training and validation sets. To preprocess these datasets and begin training a new model, run 'start_training.sh' after installation. During training, a new model will be created in the 'results/new-model/' folder. Checkpoints will be saved every 5 epochs, and training can be stopped at any point by killing the process (ctrl+c). Training can be continued from the latest checkpoint by running 'continue_training.sh'.
• The model used for the TVCG paper is also included under 'results/noof-ultraminiv12'. To test this model, run 'test_inference.sh' after installation. Output PSNR, SSIM, and images will be created in 'infer/'.