Collaborative Tooth Motion Diffusion Model in Digital Orthodontics
Authors: Yeying Fan, Guangshun Wei, Chen Wang, Shaojie Zhuang, Wenping Wang, Yuanfeng Zhou
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of Software, Shandong University, China 2Department of Computer Science, The University of Hong Kong, China 3Texas A&M University, USA |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a direct link to a code repository for the methodology. |
| Open Datasets | No | Our dataset consists of 1050 dental cases. Each dental case includes a group of models before and after alignment, the corresponding whole tooth motion process, and the tooth frames. These data are collected from hospitals and labeled by professional dentists. |
| Dataset Splits | Yes | We randomly divided the data into three parts for network training: 787 for training, 105 for validation, and 158 for testing. |
| Hardware Specification | Yes | All of them are trained on a single NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions general software components like 'neural network learning' and 'PyTorch' implicitly but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Our model is trained with N = 100 noising steps and a cosine noise schedule. All of them are trained on a single NVIDIA RTX 4090 GPU. We use zero padding to deal with the missing teeth and downsample M = 400 points for each tooth point cloud from the intraoral scan model by farthest point sampling. The dimension of the tooth latent shape code Z is z = 16. The denoising network is a GRU with four hidden layers and a latent dimension 256. The empirical parameter δ is 0.1. We weigh our loss terms by λ1 = 1, λ2 = 1, λ3 = 10, λ4 = 1, λ5 = 0.1, and λ6 = 0.1. The framework is trained in two stages. The encoding part was trained first, and then its output was used as input for training the motion generation part. |