An Implicit Parametric Morphable Dental Model
- URL: http://arxiv.org/abs/2211.11402v1
- Date: Mon, 21 Nov 2022 12:23:54 GMT
- Title: An Implicit Parametric Morphable Dental Model
- Authors: Congyi Zhang, Mohamed Elgharib, Gereon Fox, Min Gu, Christian
Theobalt, Wenping Wang
- Abstract summary: We present the first parametric 3D morphable dental model for both teeth and gum.
It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components.
Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications.
- Score: 79.29420177904022
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 3D Morphable models of the human body capture variations among subjects and
are useful in reconstruction and editing applications. Current dental models
use an explicit mesh scene representation and model only the teeth, ignoring
the gum. In this work, we present the first parametric 3D morphable dental
model for both teeth and gum. Our model uses an implicit scene representation
and is learned from rigidly aligned scans. It is based on a component-wise
representation for each tooth and the gum, together with a learnable latent
code for each of such components. It also learns a template shape thus enabling
several applications such as segmentation, interpolation, and tooth
replacement. Our reconstruction quality is on par with the most advanced global
implicit representations while enabling novel applications. Project page:
https://vcai.mpi-inf.mpg.de/projects/DMM/
Related papers
- Differentiable Collision-Supervised Tooth Arrangement Network with a Decoupling Perspective [10.293207903989053]
Existing learning-based methods use hidden teeth features to directly regress teeth motions.
We propose DTAN, a differentiable collision-supervised tooth arrangement network.
We construct three different tooth arrangement datasets and achieve drastically improved performance on accuracy and speed.
arXiv Detail & Related papers (2024-09-18T12:52:54Z) - TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs [45.0864129371874]
We propose a 3D teeth reconstruction framework, named TeethDreamer, 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.
To ensure the 3D consistency across generated views, we integrate a 3D-aware feature attention mechanism in the reverse diffusion process.
arXiv Detail & Related papers (2024-07-16T06:24:32Z) - 3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit
Functions [6.169259577480194]
Occudent is a framework for 3D teeth reconstruction from panoramic radiographs using neural implicit functions.
It is trained and validated with actual panoramic radiographs as input, distinct from recent works which used synthesized images.
arXiv Detail & Related papers (2023-11-28T05:06:22Z) - YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease
Detection [4.136033167469768]
YOLOrtho is a unified framework for teeth enumeration and dental disease detection.
We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data.
To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth.
arXiv Detail & Related papers (2023-08-11T06:54:55Z) - Construction of unbiased dental template and parametric dental model for
precision digital dentistry [46.459289444783956]
We develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
A total of 159 CBCT images of real subjects are collected to perform the constructions.
arXiv Detail & Related papers (2023-04-07T09:39:03Z) - ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D
Panoramic Image [35.72913439096702]
In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable.
In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic image.
We successfully project an input to the learned latent space via neural optimization to obtain the full tooth model conditioned on the input.
arXiv Detail & Related papers (2022-11-25T18:15:22Z) - Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and
Landmark Localization on 3D Intraoral Scans [56.55092443401416]
emphiMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.953pm0.076$, significantly outperforming the original MeshSegNet.
PointNet-Reg achieved a mean absolute error (MAE) of $0.623pm0.718, mm$ in distances between the prediction and ground truth for $44$ landmarks, which is superior compared with other networks for landmark detection.
arXiv Detail & Related papers (2021-09-24T13:00:26Z) - DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to
the Third Dimension [71.71234436165255]
We contribute DensePose 3D, a method that can learn such reconstructions in a weakly supervised fashion from 2D image annotations only.
Because it does not require 3D scans, DensePose 3D can be used for learning a wide range of articulated categories such as different animal species.
We show significant improvements compared to state-of-the-art non-rigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals.
arXiv Detail & Related papers (2021-08-31T18:33:55Z) - TSGCNet: Discriminative Geometric Feature Learning with Two-Stream
GraphConvolutional Network for 3D Dental Model Segmentation [141.2690520327948]
We propose a two-stream graph convolutional network (TSGCNet) to learn multi-view information from different geometric attributes.
We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners.
arXiv Detail & Related papers (2020-12-26T08:02:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.