Request access to the DCTrellis2 dental weights

These checkpoints were fine-tuned on Teeth3DS+, which is licensed CC BY-NC-ND 4.0. Access is reviewed manually and granted for non-commercial research only.

By requesting access you agree to the terms below. Requests are reviewed manually; incomplete or commercial-sounding requests will be declined.

DCTrellis2 — dental fine-tuned TRELLIS.2 denoisers

Two fine-tuned denoiser checkpoints that adapt TRELLIS.2 to single-photo reconstruction of dental arch casts. They are the weights used by JeremyHo1123/DCTrellis2 — a pipeline that turns one photo of a dental model into a 3D mesh and then into a uniform-thickness clear-aligner shell ready for printing.

Research use only. Not a medical device. These weights have no FDA / EU MDR / TFDA clearance and have not undergone clinical validation. They must not be used to plan, manufacture, or deliver orthodontic treatment for any patient.

Files

File	Size	Replaces in TRELLIS.2
`dental_ss_flow_64.pt`	4.9 GB	Stage 1 — sparse-structure flow denoiser @ 64³
`dental_shape_flow_1024.pt`	4.9 GB	Stage 2 — shape SLat flow denoiser (loaded into the `shape_slat_flow_model_512` slot, voxel resolution 1024)

Each file is a plain PyTorch state_dict (≈1.29 B parameters) holding the EMA weights at step 10 000. Everything else in the pipeline — the VAEs, the O-Voxel decoder, the texturing head — is stock TRELLIS.2 and is pulled from microsoft/TRELLIS.2-4B at runtime.

Usage

git clone --recursive https://github.com/JeremyHo1123/DCTrellis2.git
cd DCTrellis2
# follow docs/INSTALLATION.md to build the environment first

hf auth login
hf download Jeremy19991123/DCTrellis2-dental --local-dir weights

mkdir -p results/dental_ss_flow_64/ckpts results/dental_shape_flow_1024/ckpts
cp weights/dental_ss_flow_64.pt      results/dental_ss_flow_64/ckpts/denoiser_ema0.9999_step0010000.pt
cp weights/dental_shape_flow_1024.pt results/dental_shape_flow_1024/ckpts/denoiser_ema0.9999_step0010000.pt

python app_2.py    # must be launched from the repo root

app_2.py resolves these as relative paths from the repo root; change the SS_CKPT / SHAPE_CKPT constants near the bottom of the file if you want them elsewhere.

Training


Base checkpoints	stock TRELLIS.2 Stage-1 and Stage-2 denoisers (MIT)
Data	~1 049 single-arch dental STL casts from Teeth3DS+, rendered under domain randomisation
Optimiser	AdamW, `lr = 1e-5`, `weight_decay = 0.01`, `betas = (0.9, 0.95)`
Steps	10 000, batch size 1 per GPU
EMA	0.9999 (the released checkpoints are the EMA weights)
Gradient clipping	`AdaptiveGradClipper`, `max_norm = 1.0`, 95th percentile
Precision	bf16
Hardware	2 × NVIDIA RTX A6000 48 GB, CUDA 12.4

The exact configs live in configs/dental_finetune/ in the GitHub repo, along with the commands needed to reproduce both stages.

Hardware for inference

Tested on an RTX A6000 (48 GB). Reconstruction takes 1–2 minutes per photo; the aligner generation stage is CPU-bound and takes several minutes. Roughly 50 GB of disk is needed in total (18 GB of stock TRELLIS.2 HF cache + 9.8 GB for these weights + the conda env).

Licensing

These weights are licensed CC BY-NC-ND 4.0, inherited from the Teeth3DS+ training data:

BY — attribution required (see Citation).
NC — non-commercial use only; no commercial product, service, or clinical offering.
ND — do not distribute modified or further fine-tuned versions of these weights.

The DCTrellis2 source code is separately MIT-licensed. Full details in NOTICE.md.

Teeth3DS+ is not redistributed here in any form. To reproduce the fine-tune you must obtain it yourself from OSF and accept its licence directly.

Citation

Citation is a condition of use.

The paper describing this work is under preparation and the citation will be posted here as soon as it is available. Until then, please open an issue on the GitHub repo or start a discussion here, and the current reference will be sent to you.

You must also cite the Teeth3DS+ dataset and the TRELLIS.2 backbone:

@article{ben2022teeth3ds,
  title   = {Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans},
  author  = {Ben-Hamadou, Achraf and Smaoui, Oussama and Chaabouni-Chouayakh, Houda and
             Rekik, Ahmed and Pujades, Sergi and Boyer, Edmond and Strippoli, Julien and
             Thollot, Aur{\'e}lien and Setbon, Hugo and Trosset, Cyril and others},
  journal = {arXiv preprint arXiv:2210.06094},
  year    = {2022}
}

@article{ben20233dteethseg,
  title   = {3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge},
  author  = {Ben-Hamadou, Achraf and Smaoui, Oussama and Rekik, Ahmed and Pujades, Sergi and
             Boyer, Edmond and others},
  journal = {arXiv preprint arXiv:2305.18277},
  year    = {2023}
}