High-Fidelity 3D Tooth Reconstruction by Fusing Intraoral Scans and CBCT Data via a Deep Implicit Representation

TL;DR

This paper introduces a fully-automated deep learning pipeline that fuses intraoral scans and CBCT data to produce high-fidelity, seamless 3D tooth models capturing both crowns and roots, advancing digital dentistry.

Contribution

It presents a novel method combining deep implicit representations with hybrid proxy meshes for accurate, artifact-free 3D tooth reconstruction from multimodal data.

Findings

Successfully preserves crown detail from IOS scans.

Accurately captures root morphology from CBCT data.

Produces seamless, watertight 3D tooth models.

Abstract

High-fidelity 3D tooth models are essential for digital dentistry, but must capture both the detailed crown and the complete root. Clinical imaging modalities are limited: Cone-Beam Computed Tomography (CBCT) captures the root but has a noisy, low-resolution crown, while Intraoral Scanners (IOS) provide a high-fidelity crown but no root information. A naive fusion of these sources results in unnatural seams and artifacts. We propose a novel, fully-automated pipeline that fuses CBCT and IOS data using a deep implicit representation. Our method first segments and robustly registers the tooth instances, then creates a hybrid proxy mesh combining the IOS crown and the CBCT root. The core of our approach is to use this noisy proxy to guide a class-specific DeepSDF network. This optimization process projects the input onto a learned manifold of ideal tooth shapes, generating a seamless, watertight, and anatomically coherent model. Qualitative and quantitative evaluations show our method uniquely preserves both the high-fidelity crown from IOS and the patient-specific root morphology from CBCT, overcoming the limitations of each modality and naive stitching.