VLDB 2026 Research / reviewers in the wild / expert
Yuer Ye
dblp:415/6521
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering
image-based rendering |
0.9 | 1 | 2025 | AI4TRT: Automatic Simulation of Teeth Restoration Treatment · IJCAI 2025 |
Virtual and augmented reality › tracking
camera pose estimation |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AI4TRT: Automatic Simulation of Teeth Restoration TreatmentabstractVisualizing 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 |
IJCAI | 2 |