README.md

metadata

license: cc-by-nc-4.0
tags:
  - diffusion-model
  - 3d-generation
  - dental
  - medical-imaging
  - signed-distance-function
  - pytorch
library_name: pytorch

ToothCraft

---

license: cc-by-nc-4.0 tags: - diffusion-model - 3d-generation - dental - medical-imaging - signed-distance-function - pytorch library_name: pytorch

ToothCraft

ToothCraft is a diffusion model for patient-specific dental crown completion — it reconstructs a missing or damaged tooth crown as a 3D shape, conditioned on the surrounding (local) anatomical context of the dental arch. It is the official pretrained-weights release accompanying the paper "From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion" (VISAPP 2026).

• 📄 Paper: arXiv:2603.26588
• 💻 Code: github.com/ikarus1211/VISAPP_ToothCraft
• ✍️ Authors: Dávid Pukanec, Tibor Kubík, Michal Španěl

Model description

ToothCraft learns to complete incomplete tooth geometry by running a diffusion process over Signed Distance Functions (SDFs), conditioned on a local context cutout that includes the neighboring crowns in the arch. The goal is to generate a crown that fits both the local morphology of the patient's dentition and the occlusion with the opposing (antagonist) teeth, reducing the risk of occlusal interference in real restorations.

Because paired real damaged/complete crown data is scarce, the model was trained primarily on synthetically damaged teeth: an augmentation pipeline carves incomplete geometry out of complete dental arches from the public 3DS and ODD datasets, which the model then learns to complete. The paper also reports that the resulting model transfers directly to real-world restoration cases.

Checkpoints in this repository

File	Variant	Trained on	Notes
ToothCraft_normal_control_branch.pth	Normal	ODD	Control-branch weights
ToothCraft_normal_diff_branch.pth	Normal	ODD	Diffusion-branch weights
ToothCraft_classfree_control_branch.pth	Classifier-free	ODD	Control-branch weights
ToothCraft_classfree_diff_branch.pth	Classifier-free	ODD	Diffusion-branch weights
ToothCraft_antag_control_branch.pth	Antagonist-aware	ODD	Control-branch weights
ToothCraft_antag_diff_branch.pth	Antagonist-aware	ODD	Diffusion-branch weights
ToothCraft_gingiva-mod_control_branch.pth	Gingiva-aware ⭐	ODD	Control-branch weights
ToothCraft_gingiva-mod_diff_branch.pth	Gingiva-aware ⭐	ODD	Diffusion-branch weights

Note: each of the variants ships a control branch and a diffusion branch checkpoint, which are loaded together at inference time.

⭐ Recommended for clinical cases: the gingiva-aware (gingiva-mod) variant reports improved results and is the best-suited checkpoint for real/clinical restorations, where the tooth does not properly connect to gingiva.

Usage

Inference and training code, configuration files, and the data-augmentation pipeline are provided in the companion GitHub repository: ikarus1211/VISAPP_ToothCraft. Download the checkpoint(s) for the variant you need from this repository and point the relevant config (e.g. configs/test/test_normal.yaml) at the local file paths, then run:

python test.py --config-path "configs/test" --config-name "test_normal.yaml"

See the GitHub README for environment setup (Python 3.10, PyTorch 2.9, Hydra-managed configs).

Training data

• 3DS and ODD public dental arch datasets, processed with the repository's AugmentPipeline/ to synthesize incomplete crowns from complete arches for training and evaluation.

License

This model (code is licensed separately — see the GitHub repository) is released under CC BY-NC 4.0: you may share and adapt it with attribution, for non-commercial purposes only. Contact the authors for commercial licensing.

In addition to the rights granted under the CC BY-NC 4.0 license, we permit commercial entities to internally test and evaluate the model for a period of 90 days from the date the model is first obtained, provided that the model is not deployed in production and its outputs are not provided to third parties. For any other commercial use, please get in touch with the authors.

Citation

@inproceedings{pukanec2026toothcraft,
  title     = {From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion},
  author    = {Pukanec, D\'avid and Kub\'ik, Tibor and \v{S}pan\v{e}l, Michal},
  booktitle = {Proceedings of VISAPP 2026},
  year      = {2026},
  doi       = {10.5220/0014646500004084}
}