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

Yan Tian, Pengcheng Xue, Weiping Ding

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    Summary
    This summary is machine-generated.

    This study introduces DM-CFO, a new method for generating 3D tooth models. It addresses challenges in compositional 3D tooth generation and prevents collisions, improving realism and consistency.

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    Area of Science:

    • * Dental Digitalization
    • * Computer Graphics
    • * Artificial Intelligence

    Background:

    • * Current 3D tooth generation methods struggle with optimizing the layout and shape of missing teeth.
    • * 3D Gaussian-based generation often overlooks collision detection, leading to intersecting objects.
    • * Explicit geometric information is frequently absent in existing 3D generation techniques.

    Purpose of the Study:

    • * To develop an advanced approach for compositional 3D tooth generation that optimizes both layout and shape.
    • * To introduce a method that effectively prevents collisions between generated 3D tooth models.
    • * To enhance the realism and multiview consistency of automatically designed 3D dental models.

    Main Methods:

    • * Proposed DM-CFO approach combining graph generation via diffusion models and 3D Gaussian-based collision detection.
    • * Progressive restoration of missing tooth layouts during denoising, guided by text and graph constraints.
    • * Alternating updates of Gaussian parameters and jaw structure using score distillation sampling (SDS).
    • * Introduced a regularization term to penalize intersections based on 3D Gaussian distances.

    Main Results:

    • * DM-CFO demonstrated significant improvements in multiview consistency and realism on three tooth-design datasets.
    • * The method successfully addressed challenges in compositional 3D tooth generation.
    • * Collision conflicts were effectively penalized and reduced in the generated 3D models.

    Conclusions:

    • * DM-CFO offers a robust solution for compositional 3D tooth generation in dental digitization.
    • * The approach enhances the quality and accuracy of automatically designed 3D dental models.
    • * This method represents a significant advancement in creating realistic and collision-free 3D tooth models.