Implementation of Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds. If using this code, please cite the paper.
Run training by calling 'train.py' with a specificed config file, for example:
python train.py -c cfgs/point2ssm++.yaml
This will write the model, logged info, and a copy of the config file to a folder in experiments/, such as experiments/spleen_all/point2ssm++_cd_l2_dgcnn/.
To run inference, call consist_test.py with the config file and dataset, for example:
python consist_test.py -c experiments/spleen_all/point2ssm++_cd_l2_dgcnn/point2ssm++.yaml -d spleen
This will write the predicted correspondence points to the experiment directory, for example experiments/spleen_all/point2ssm++_cd_l2_dgcnn/spleen/test/output/.
See cfgs/point2ssm++_4d.yaml for an example with 4D/spatiotemporal data, and cfgs/point2ssm++_classifier.yaml for multi-anatomy data.
This code utilizes the following Pytorch 3rd-party libraries and models: