DM-CFO: A Diffusion Model for Compositional 3D Tooth Generation with Collision-Free Optimization

TL;DR

This paper introduces DM-CFO, a diffusion model that generates collision-free, realistic 3D tooth models by optimizing layouts and shapes with explicit geometric constraints, improving dental digitization processes.

Contribution

The paper presents a novel diffusion-based approach for compositional 3D tooth generation that incorporates collision-free optimization using Gaussian parameters and graph constraints.

Findings

Significantly improves multiview consistency of generated teeth

Enhances realism of 3D tooth models

Reduces tooth intersection conflicts

Abstract

The automatic design of a 3D tooth model plays a crucial role in dental digitization. However, current approaches face challenges in compositional 3D tooth generation because both the layouts and shapes of missing teeth need to be optimized.In addition, collision conflicts are often omitted in 3D Gaussian-based compositional 3D generation, where objects may intersect with each other due to the absence of explicit geometric information on the object surfaces. Motivated by graph generation through diffusion models and collision detection using 3D Gaussians, we propose an approach named DM-CFO for compositional tooth generation, where the layout of missing teeth is progressively restored during the denoising phase under both text and graph constraints. Then, the Gaussian parameters of each layout-guided tooth and the entire jaw are alternately updated using score distillation sampling (SDS). Furthermore, a regularization term based on the distances between the 3D Gaussians of neighboring teeth and the anchor tooth is introduced to penalize tooth intersections. Experimental results on three tooth-design datasets demonstrate that our approach significantly improves the multiview consistency and realism of the generated teeth compared with existing methods. Project page: https://amateurc.github.io/CF-3DTeeth/.