Demo Data from EDBT 2026 / reviewers in the wild / expert
Shuh-Yuan Liou
dblp:56/3983
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering
engineering design |
0.0 | 1 | 2000 | Knowledge based product life cycle systems: principles of integration of KBE and C3P · Comput. Aided Des. 2000 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | A Complete Distance Field RepresentationabstractDistance fields are an important volume representation. A high quality distance field facilitates accurate surface characterization and gradient estimation. However, due to Nyquist's law, no existing volumetric methods based on the linear sampling theory can fully capture surface details, such as comers and edges, in 3D space. We propose a novel complete distance field representation (CDFR) that does not rely on Nyquist's sampling theory. To accomplish this, we construct a volume where each voxel has a complete description of all portions of surface that affect the local distance field. For any desired distance, we are able to extract a surface contour in true Euclidean distance, at any level of accuracy, from the same CDFR representation. Such point-based iso-distance contours have faithful per-point gradients and can be interactively visualized using splatting, providing per-point shaded image quality. We also demonstrate applying CDFR to a cutting edge design for manufacturing application involving high-complexity parts at unprecedented accuracy using only commonly available computational resources. Roger Crawfis, Shao-Chiung Lu, Shuh-Yuan Liou |
IEEE Visualization | 5 |
| 2000 | Knowledge based product life cycle systems: principles of integration of KBE and C3P
J. A. Penoyer, G. Burnett, D. J. Fawcett, Shuh-Yuan Liou |
Comput. Aided Des. | 4 |