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

TL;DR

This work proposes 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, and significantly improves the multiview consistency and realism of the generated teeth compared with existing methods.

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/.

Figures (13)

• Figure 1: Illustration of approaches using the pairwise relations and the proposed approach. (a) GALA3D uses only the text description to optimize the layout (teeth positions), receiving limited results. (b) Our DM-CFO constructs a graph to represent the jaw with multiple missing teeth, then the target graph is incrementally restored during the denoising process through a graph diffusion model.
• Figure 2: An illustration of the proposed DM-CFO. Given 3D Gaussian representing jaw with missing teeth, the graph diffusion generates layout of missing teeth by progressively denoising graph with both text and graph constraints. Then, the Gaussian parameters of each layout-guided tooth and the whole jaw are alternately updated using SDS. A regularization term based on 3D Gaussians of neighboring teeth is explored to penalize the tooth intersection.
• Figure 3: An illustration of the layout editing module. Given the original jaw graph, noise is progressively added in the forward diffusion process, and then graph is progressively updated in the reverse denoising process to obtain the augmented jaw graph. The yellow arrow represents the iteration step.
• Figure 4: An illustration of the compositional optimization module. Instance-level diffusion and scene-level diffusion are jointly optimized.
• Figure 5: An illustration of collision loss. Our approach employs an intravariance $R_i$ rather than a fixed threshold to avoid collision conflict.