|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM378349384 |
| 003 |
DE-627 |
| 005 |
20241001233159.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
241001s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2024.3470992
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1554.xml
|
| 035 |
|
|
|a (DE-627)NLM378349384
|
| 035 |
|
|
|a (NLM)39348253
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Xie, Jiaming
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Tooth Motion Monitoring in Orthodontic Treatment by Mobile Device-based Multi-view Stereo
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 30.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a Nowadays, orthodontics has become an important part of modern personal life to assist one in improving mastication and raising self-esteem. However, the quality of orthodontic treatment still heavily relies on the empirical evaluation of experienced doctors, which lacks quantitative assessment and requires patients to visit clinics frequently for in-person examination. To resolve the aforementioned problem, we propose a novel and practical mobile device-based framework for precisely measuring tooth movement in treatment, so as to simplify and strengthen the traditional tooth monitoring process. To this end, we formulate the tooth movement monitoring task as a multi-view multi-object pose estimation problem via different views that capture multiple texture-less and severely occluded objects (i.e. teeth). Specifically, we exploit a pre-scanned 3D tooth model and a sparse set of multi-view tooth images as inputs for our proposed tooth monitoring framework. After extracting tooth contours and localizing the initial camera pose of each view from the initial configuration, we propose a joint pose estimation scheme to precisely estimate the 3D pose of each individual tooth, so as to infer their relative offsets during treatment. Furthermore, we introduce the metric of Relative Pose Bias to evaluate the individual tooth pose accuracy in a small scale. We demonstrate that our approach is capable of reaching high accuracy and efficiency as practical orthodontic treatment monitoring requires
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Zhang, Congyi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wei, Guangshun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Peng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wei, Guodong
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Wenxi
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Gu, Min
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Luo, Ping
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Wenping
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2024) vom: 30. Sept.
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2024
|g day:30
|g month:09
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2024.3470992
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2024
|b 30
|c 09
|