Sara Fridovich-Keil, Fabrizio Valdivia, Gordon Wetzstein, Benjamin Recht, Mahdi Soltanolkotabi
arXiv: https://arxiv.org/abs/2310.03956
Accepted for publication in IEEE Transactions on Information Theory
This repo is built on a fork from the JAX version of Plenoxels: Radiance Fields without Neural Networks, with modification to support X-ray cone beam computed tomography (CBCT) and compare linear vs nonlinear reconstruction methods. Note that, contrary to its name, the focus of this repo is on single-energy CT and comparing linear (post-log) versus nonlinear (no-log) CT reconstruction.
We recommend setup with a conda environment, using the packages provided in requirements.txt.
The training file is p4_copy2.py which is a slightly modified variant of plenoptimize.py; its flags specify many options to control the optimization (scene, resolution, training duration, when to prune and subdivide voxels, where the training data is, where to save rendered images and model checkpoints, etc.). You can also set the frequency of evaluation, which will compute the validation PSNR and render validation images (comparing the reconstruction to the ground truth). The Jupyter notebooks are primarily used for figure generation.