Yuer Ye

dblp:415/6521 · 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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, 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
Rendering · 77% Virtual and augmented reality · 23%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering
image-based rendering
0.912025
AI4TRT: Automatic Simulation of Teeth Restoration Treatment · IJCAI 2025
Virtual and augmented reality › tracking
camera pose estimation
0.312025
AI4TRT: Automatic Simulation of Teeth Restoration Treatment · IJCAI 2025

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

point-based rendering · 1.7optical flow · 1.7
YearPublicationVenuePosition
2025 AI4TRT: Automatic Simulation of Teeth Restoration Treatment
abstract
Visualizing restoration treatments is a crucial task in dentistry. Traditionally, dentists drag the standard template tooth line onto the inner image from the front view to simulate the outcome of the restoration. This process lacks the precision needed for patient presentation. We find that calculating the camera pose and the relative positions of the upper and lower jaws can enhance visualization accuracy and efficiency while assisting dentists in treatment design. In this work, we leverage the optical flow model and a customized point renderer to help dentists show the treatment outcome to the patient. Specifically, we take the 3D scan model and the intraoral image pair as input. Our framework automatically outputs the camera pose and the relative position of the upper and lower jaws. With these parameters, dentists can directly design the restoration treatment on the 3D scan model without caring about the 2D visualization. Then the designed tooth line and other simulation modalities can be rendered on the intraoral image with our customized renderer. Our framework relieves the labor of dentists and shows the case precisely.
Feihong Shen, Yuer Ye
IJCAI2