Demo Data from EDBT 2026 / reviewers in the wild / expert
Taegwan Kim
dblp:431/4948
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
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 · 87% Geometric modeling and processing · 13% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d scene reconstruction |
1.0 | 1 | 2026 | Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026 |
Computer vision › 3D vision › 3d reconstruction › surface reconstruction
water surface reconstruction |
1.0 | 1 | 2026 | Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026 |
Rendering
gaussian splatting |
1.0 | 1 | 2026 | Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026 |
Rendering
neural rendering |
1.0 | 1 | 2026 | Through the Water: Refractive Gaussian Splatting for Water Surface Scenes · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
soft mask · 2.0dual gaussian primitives · 2.02d gaussian ray tracing · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Through the Water: Refractive Gaussian Splatting for Water Surface ScenesabstractScenes with water surfaces present a significant challenge for Gaussian Splatting due to the simultaneous presence of refraction and reflection, as well as the difficulty of accurately estimating the geometry of transparent water surfaces. To address this, we propose a novel framework for reconstructing scenes involving both reflection and refraction caused by water surfaces. The water surface is modeled as a trainable plane, and 2D Gaussian ray tracing is applied to account for refraction through the water. We extend 2D Gaussian Splatting by introducing a soft mask parameter and a dual set of Gaussian primitives, which handle both reflected and refracted effects. Our method achieves state-of-the-art performance on newly constructed water surface datasets, including both synthetic and real scenes, and significantly outperforms prior approaches in water-interacting regions. Furthermore, we demonstrate the editability of our model by manipulating the index of refraction to suppress or modify refractive effects, enabling scene transformations into different liquids. Yeonghun Yoon, Hojoon Jung, Jaeyoon Lee, Taegwan Kim, Gyu-Hyun Kim |
AAAI | 4 |