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A High-Fidelity Framework for 3D Dental Crown Reconstruction Using Intraoral Images.

Ni Liao1, Xinyu Zhang1, Wenkang Chen2

  • 1Department of Orthodontics, College & Hospital of Stomatology, Guangxi Medical University, Nanning, Guangxi, 530021, China.

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|January 28, 2026
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Summary

DenGaussDiff precisely reconstructs 3D dental crowns using Gaussian neural fields and diffusion models from minimal intraoral images. This framework enhances digital dentistry for improved orthodontic diagnosis and monitoring.

Keywords:
Digital orthodonticsIntraoral image-based generationNeural representationsSparse-view 3D reconstruction

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Dental Technology

Background:

  • Accurate 3D dental crown reconstruction is crucial for diagnosis and treatment planning.
  • Existing methods struggle with precision using limited intraoral images.

Purpose of the Study:

  • Develop DenGaussDiff, a novel framework for precise 3D dental crown reconstruction.
  • Utilize three-dimensional (3D) Gaussian neural fields and diffusion priors.
  • Reconstruct dental crowns from only five intraoral images.

Main Methods:

  • Collected 1000 clinical cases with 5 intraoral images and intraoral scanning (IOS) models.
  • Employed image segmentation, enhancement, and ControlNet++ for 3D representation.
  • Optimized 3D models using multiscale Gaussian rendering and Iterative Closest Point (ICP) registration.

Main Results:

  • DenGaussDiff outperformed existing methods, improving PSNR by 1.33% and SSIM by 3.9%.
  • Achieved high-precision registration between reconstructed and IOS models.
  • Demonstrated excellent performance on public datasets for detecting dental issues.

Conclusions:

  • DenGaussDiff enables precise 3D dental crown reconstruction from sparse intraoral images.
  • Shows potential for orthodontic diagnosis, monitoring, and digital dentistry implementation.