| import argparse |
| from sugar_utils.general_utils import str2bool |
| from sugar_trainers.coarse_density import coarse_training_with_density_regularization |
|
|
|
|
| if __name__ == "__main__": |
| |
| parser = argparse.ArgumentParser(description='Script to optimize a coarse SuGaR model, i.e. a 3D Gaussian Splatting model with surface regularization losses in density space.') |
| parser.add_argument('-c', '--checkpoint_path', |
| type=str, |
| help='path to the vanilla 3D Gaussian Splatting Checkpoint to load.') |
| parser.add_argument('-s', '--scene_path', |
| type=str, |
| help='path to the scene data to use.') |
| parser.add_argument('-o', '--output_dir', |
| type=str, default=None, |
| help='path to the output directory.') |
| parser.add_argument('-i', '--iteration_to_load', |
| type=int, default=7000, |
| help='iteration to load.') |
| |
| parser.add_argument('--eval', type=str2bool, default=True, help='Use eval split.') |
| parser.add_argument('--white_background', type=str2bool, default=False, help='Use a white background instead of black.') |
| |
| parser.add_argument('-e', '--estimation_factor', type=float, default=0.2, help='factor to multiply the estimation loss by.') |
| parser.add_argument('-n', '--normal_factor', type=float, default=0.2, help='factor to multiply the normal loss by.') |
| |
| parser.add_argument('--gpu', type=int, default=0, help='Index of GPU device to use.') |
|
|
| args = parser.parse_args() |
| |
| |
| coarse_training_with_density_regularization(args) |
| |
