3D Geometric Tooth Alignment Planning via Deep Reinforcement Learning
Quick Answer
This paper introduces a deep reinforcement learning framework for 3D geometric tooth alignment, utilizing the DDPG algorithm and a Transformer-based agent.
Quick Take
This paper introduces a deep reinforcement learning framework for 3D geometric tooth alignment, utilizing the DDPG algorithm and a Transformer-based agent. The method significantly enhances path safety and geometric efficiency, outperforming existing baselines on a dataset of 10K expert-designed treatment plans.
Key Points
- Utilizes a Markov Decision Process to optimize sequential tooth alignment trajectories.
- Implements a dynamic masking scheme for selective tooth movement during alignment.
- Employs a two-stage curriculum learning strategy for improved training stability.
- Outperforms existing methods in path safety and geometric efficiency.
- Evaluated on a dataset of 10,000 expert-designed orthodontic treatment plans.
Paper Resources
📖 Reader Mode
~2 min readAbstract:3D geometric tooth alignment planning, which determines sequential trajectories from initial malocclusion to the final target alignment, is a cornerstone of modern digital orthodontics. This paper presents a novel deep reinforcement learning (DRL) framework to automate the generation of these alignment paths. We formulate the planning process as a Markov Decision Process (MDP) to capture its sequential decision-making nature, focusing on optimizing geometric trajectories while integrating essential spatial constraints, such as inter-dental collision avoidance and path efficiency. The proposed method leverages the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by three key innovations: (1) a Transformer-based agent to model complex spatial interactions between teeth and manage high-dimensional state-action spaces; (2) a dynamic masking scheme that restricts movement to a sparse subset of teeth per step, better reflecting the clinical logic of sequential alignment; and (3) a two-stage curriculum learning strategy that gradually increases task difficulty to ensure training stability and efficient path discovery. We evaluate our approach on a dataset of 10K expert-designed treatment plans based on clinical data. Experimental results demonstrate that our method outperforms existing baselines in terms of path safety and geometric efficiency, providing a robust and automated solution for 3D geometric orthodontic alignment planning.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.14544 [cs.CV] |
| (or arXiv:2607.14544v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.14544 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianwen Lou [view email]
[v1]
Thu, 16 Jul 2026 04:00:05 UTC (12,738 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.