bibtext: @InProceedings{ Xu_TeethDreamer_MICCAI2024,
                   author = { Xu, Chenfan and Liu, Zhentao and Liu, Yuan and Dou, Yulong and Wu, Jiamin and Wang, Jiepeng and Wang, Minjiao and Shen, Dinggang and Cui, Zhiming },
                   title = { { TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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 attention mechanism in the reverse diffusion process. Moreover, a geometry-aware normal loss is incorporated into the teeth reconstruction process to enhance geometry accuracy. Extensive experiments demonstrate the superiority of our
  method over current state-of-the-arts, giving the potential to monitor orthodontic treatment remotely. Our code is available at https://github.com/ShanghaiTech-IMPACT/TeethDreamer.
title: TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs
authors: Xu, Chenfan Liu, Zhentao Liu, Yuan Dou, Yulong Wu, Jiamin Wang, Jiepeng Wang, Minjiao Shen, Dinggang Cui, Zhiming