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Source code & pretrained model for "PointTriNet: Learned Triangulation of 3D Point Sets", by Nicholas Sharp and Maks Ovsjanikov at ECCV 2020.

demo gif

Example: Generate a mesh

The script main_generate_mesh.py applies a trained model to triangulate a point set. A set of pretrained weights are included in saved_models/

python src/main_generate_mesh.py saved_models/model_state_dict.pth path/to/points.ply

Check out the --help flag on the script for arguments. In particular, the script can either take a point cloud directly as input, or take a mesh as input and uniformly sample points with --sample_cloud.

Note that by default, the script opens up a GUI (using Polyscope) to show results. To skip the GUI and just write out the resulting mesh, use:

python src/main_generate_mesh.py path_to_your_cloud_or_mesh.ply --output result

Example: Integrating with code

If you want to integrate PointTriNet in to your own codebase, the PointTriNet_Mesher from point_tri_net.py encapsulates all the functionality of the method. It's a torch.nn.Module, so you can make it a member of other modules, load weights, etc.

To create the model, load weights, and triangulate a point set, just call:

model = PointTriNet_Mesher()
model.load_state_dict(torch.load(some_path))
model.eval()

samples = # your (B,V,3) torch tensor of point positions

with torch.no_grad():
  candidate_triangles, candidate_probs = model.predict_mesh(samples)
  # candidate_triangles is a (B, F, 3) index tensor, predicted triangles
  # candidate_probs is a (B, F) float tensor of [0,1] probabilities for each triangle

  # You are probably interested in only the high-probability triangles. For example,
  # get the high-probability triangles from the 0th batch entry like
  b = 0
  prob_thresh = 0.9
  high_prob_faces = candidate_triangles[b, candidate_probs[b,:] > prob_thresh, :]

Example: Generate data & train the model

Prerequisite: a collection of shapes to train on; we use the training set (all classes) of ShapeNet v2, which you can download on your own. Note that we do not train PointTriNet to match the triangulation of existing meshes, we're just using meshes as a convenient data source from which to sample point cloud patches.

Step 1 Sample point cloud patches as training (and validation) data

python src/generate_local_points_dataset.py --input_dir=/path/to/train_meshes/ --output_dir=data/train/ --n_samples=20000

python src/generate_local_points_dataset.py --input_dir=/path/to/val_meshes/ --output_dir=data/val/ --n_samples=5000

Step 2 Train the model

python src/main_train_model.py

With default parameters, this will train for 3 epochs on the dataset above, using < 8GB gpu memory and taking ~6hrs on an RTX 2070 GPU. Checkpoints will be saved in ./training_runs, along with tensorboard logging.

Note that this script has paths at the top relative to the expected directory layout of this repo. If you want to use a different directory layout, you can update the paths.

Dependencies

Depends on pytorch, torch-scatter, libigl, and polyscope, along with some other typical numerical components. The code is pretty standard, and there shouldn't be any particularly strict version requirements on these dependencies; any recent version should work fine.

For completeness, an environment.yml file is included (which is a superset of the required packages).

Citation

If this code contributes to academic work, please cite:

@inproceedings{sharp2020ptn,
  title={"PointTriNet: Learned Triangulation of 3D Point Sets"},
  author={Sharp, Nicholas and Ovsjanikov, Maks},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={},
  year={2020}
}

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Source code for "PointTriNet: Learned Triangulation of 3D Point Sets", by Nicholas Sharp and Maks Ovsjanikov at ECCV 2020

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