TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs
Chenfan Xu, Zhentao Liu, Yuan Liu, Yulong Dou, Jiamin Wu, Jiepeng, Wang, Minjiao Wang, Dinggang Shen, Zhiming Cui

TL;DR
TeethDreamer is a novel framework that reconstructs accurate 3D dental models from only five intra-oral photographs by leveraging diffusion models, neural surface reconstruction, and geometry-aware losses for remote orthodontic monitoring.
Contribution
The paper introduces a new method combining diffusion models and neural surface reconstruction to generate 3D teeth models from sparse intra-oral images, addressing a key challenge in remote dental monitoring.
Findings
Outperforms existing methods in 3D teeth reconstruction accuracy
Generates high-quality 3D models from only five images
Enables effective remote orthodontic treatment monitoring
Abstract
Orthodontic treatment usually requires regular face-to-face examinations to monitor dental conditions of the patients. When in-person diagnosis is not feasible, an alternative is to utilize five intra-oral photographs for remote dental monitoring. However, it lacks of 3D information, and how to reconstruct 3D dental models from such sparse view photographs is a challenging problem. In this study, we propose a 3D teeth reconstruction framework, named TeethDreamer, aiming to restore the shape and position of the upper and lower teeth. Given five intra-oral photographs, our approach first leverages a large diffusion model's prior knowledge to generate novel multi-view images with known poses to address sparse inputs and then reconstructs high-quality 3D teeth models by neural surface reconstruction. To ensure the 3D consistency across generated views, we integrate a 3D-aware feature…
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Taxonomy
TopicsDental Radiography and Imaging · Digital Imaging in Medicine · dental development and anomalies
MethodsSoftmax · Attention Is All You Need · Diffusion
