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4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ for download here.

To start training a SIREN, run:
```
python experiments_scripts/train_single_sdf.py --model_type=sine --point_cloud_path=<path_to_the_model_in_xyz_format> --batch_size=250000 --experiment_name=experiment_1
python experiments_scripts/train_sdf.py --model_type=sine --point_cloud_path=<path_to_the_model_in_xyz_format> --batch_size=250000 --experiment_name=experiment_1
```
This will regularly save checkpoints in the directory specified by the rootpath in the script, in a subdirectory "experiment_1".
The batch_size is typically adjusted to fit in the entire memory of your GPU.
Expand All @@ -106,7 +106,7 @@ To inspect a SDF fitted to a 3D point cloud, we now need to create a mesh from t
This is performed with another script that uses a marching cubes algorithm (adapted from the DeepSDF github repo)
and creates the mesh saved in a .ply file format. It can be called with:
```
python experiments_scripts/test_single_sdf.py --checkpoint_path=<path_to_the_checkpoint_of_the_trained_model> --experiment_name=experiment_1_rec
python experiments_scripts/test_sdf.py --checkpoint_path=<path_to_the_checkpoint_of_the_trained_model> --experiment_name=experiment_1_rec
```
This will save the .ply file as "reconstruction.ply" in "experiment_1_rec" (be patient, the marching cube meshing step takes some time ;) )
In the event the machine you use for the reconstruction does not have enough RAM, running test_sdf script will likely freeze. If this is the case,
Expand Down