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Noha A. SAID

dblp:415/9429 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
shape optimization
0.912025
A Rule-Based Optimization Method for Tooth Alignment · IEEE Trans. Vis. Comput. Graph. 2025
Medical and health informatics
digital dentistry
0.312025
A Rule-Based Optimization Method for Tooth Alignment · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

rule-based optimization · 1.76-dof transformation optimization · 1.7
YearPublicationVenuePosition
2025 A Rule-Based Optimization Method for Tooth Alignment
abstract
While tooth alignment is crucial for digital dentistry, especially in orthodontic treatment, existing computer-aided methods mainly focus on the 3D dental crown but overlook the entire teeth, which is essential for applications in orthodontics. Besides, clinical orthodontic rules are not fully considered in these methods, i.e., there should be no collisions and gaps between teeth, the upper jaw and lower jaw should have correct occlusion relationships, the teeth should comply with a reasonable dental arch curve, etc. To generate optimal tooth alignment results, we propose a rule-based optimization method for solving the tooth alignment problem that takes into consideration the clinical rules functionally and aesthetically. We optimize rule-driven objective functions by adjusting the 6-DoF transformations of each tooth. Besides, our optimization formulation supports customization for different clinical scenarios by specifying the various energy terms. Extensive experiments, ablation studies, and user studies have been conducted to validate the effectiveness of our method. Quantitative and qualitative comparisons demonstrate that our method generates better tooth alignments than previous methods.
Yuhan Ping, Guodong Wei, Guangshun Wei, Congyi Zhang 0001, Noha A. SAID, Jia Pan 0001, Shi-Qing Xin, Yuanfeng Zhou, Changhe Tu, Min Gu 0003, Wenping Wang 0001
IEEE Trans. Vis. Comput. Graph.5