"3D Teeth Scan Segmentation via Rotation-Invariant Descriptor"

• PyTorch 1.8.0
• Requirements

  pip install -r requirements.txt

• Before training, You can load pre-sampled dataset(.npy), or sample every epoch
  • For pre-sampling, see "./preprocessing.ipynb"

python main.py \
  [--resume "checkpoint path"]

• You can choose one of three sampling methods.
  1. Farthest Point Sampling(FPS)
  2. Poisson Disk Sampling (Creating new vertices)
  3. Poisson Disk based Simplification (Keeping existing vertices, Using this sampling method)

python inference.py

• Visualization using PyVista remehsing, and Open3D visualization

• Color gingiva and each number of teeth a different color.

• Example

  

python eval.py

• Teeth Segmentation Accuracy, 'F1'

• $precision={TP \over TP+FP},~~~~recall={TP \over TP+FN}$

  $$TSA = 2\times{precision\times recall \over precision+recall}$$

• Teeth Identification Rate, 'ACC' $$TIR={TP+TN \over TP+TN+FP+FN}$$

• Intersection over Union, 'IOU' $$IoU = {TP \over TP+FP+FN}$$

Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

python main_pointnet.py \
  [--resume "checkpoint path"]

python inference.py --model pointnet

python eval.py --model pointnet

Zhao, Hengshuang, et al. "Point transformer." Proceedings of the IEEE/CVF international conference on computer vision. 2021.

python main_pointtransformer.py \
  [--resume "checkpoint path"]

python inference.py --model pointtransformer

python eval.py --model pointtransformer

• https://github.com/limhoyeon/ToothGroupNetwork.git
• https://github.com/haoyu94/RoITr.git
• https://github.com/yanx27/Pointnet_Pointnet2_pytorch
• https://github.com/POSTECH-CVLab/point-transformer