Demo Data from EDBT 2026 / reviewers in the wild / expert
Shoichi Tsuchie
dblp:116/6731
· DBLP profile ↗
9ranked-venue papers
9as first author
4since 2021 · last 2026
0000-0002-9991-5635ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 9 · 9 first-author · 4 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
3 papers |
Geometric modeling and processing · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › computer-aided design › computer-aided geometric design
curve fairing |
1.0 | 1 | 2026 | A new measure of fairness for curves · Comput. Aided Des. 2026 |
Geometric modeling and processing
curve fitting |
0.2 | 1 | 2016 | High-quality quadratic curve fitting for scanned data of styling design · Comput. Aided Des. 2016 |
Geometric modeling and processing
mesh segmentation |
0.2 | 1 | 2014 | High-quality vertex clustering for surface mesh segmentation using Student-t mixture model · Comput. Aided Des. 2014 |
Geometric modeling and processing › mesh processing › mesh simplification
vertex clustering |
0.2 | 1 | 2014 | High-quality vertex clustering for surface mesh segmentation using Student-t mixture model · Comput. Aided Des. 2014 |
Methods — techniques the papers use, named apart from their topics
student-t mixture model · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A new measure of fairness for curvesabstractThis paper proposes a novel measure based on curvature variation to evaluate the fairness of curves. It is demonstrated that, in the simplest case, controlling the curvature using the proposed measure results in the log-aesthetic curve (LAC). In other words, by utilizing the proposed measure as a novel shape parameter, a unified framework can be established for aesthetic curves that accommodates a broader range of curvature variations, encompassing the LAC as a special case. Several examples are presented to illustrate curve evaluation using the proposed measure, along with its application to the approximation of aesthetic curves. The findings of this study offer a new perspective for understanding and evaluating the geometric properties of curves, with potential applications in curve design, analysis, and fairing. Shoichi Tsuchie |
Comput. Aided Des. | 1 |
| 2024 | α-Curves: Extended log-aesthetic curves with variable shape parameterabstractThis paper proposes a novel curve called the α-curve, which is specified by a variable shape parameter α and can be applied in curve reconstruction involving curve fairing, fitting, and segmentation. In contrast to classical log-aesthetic curves in which α is constant, this study contributes a novel formulation of α that varies monotonically within a specified segment. By introducing the variable α, the logarithmic curvature graph (LCG) becomes piecewise linear and parabolic, or any other function if necessary. Consequently, in comparison to conventional log-aesthetic curves that have limited field usage based on their rigidity, α-curves have various LCGs and flexible representations, thereby opening up many practical applications. Experimental results and comparative studies on curves created by CAD experts in the automotive industry demonstrate the theoretical and practical validity of α-curves. Shoichi Tsuchie, Norimasa Yoshida |
Comput. Graph. | 1 |
| 2023 | Reconstruction of aesthetically smooth curves
Shoichi Tsuchie |
Vis. Comput. | 1 |
| 2022 | Reconstruction of adaptive swept surfaces from scanned data for styling design
Shoichi Tsuchie |
Vis. Comput. | 1 |
| 2017 | Reconstruction of underlying surfaces from scanned data using lines of curvature
Shoichi Tsuchie |
Comput. Graph. | 1 |
| 2017 | Reconstruction of underlying curves with styling radius corners
Shoichi Tsuchie |
Vis. Comput. | 1 |
| 2016 | High-quality quadratic curve fitting for scanned data of styling design
Shoichi Tsuchie, Kunio Okamoto |
Comput. Aided Des. | 1 |
| 2014 | High-quality vertex clustering for surface mesh segmentation using Student-t mixture model
Shoichi Tsuchie, Tikara Hosino, Masatake Higashi |
Comput. Aided Des. | 1 |
| 2012 | Surface mesh denoising with normal tensor framework
Shoichi Tsuchie, Masatake Higashi |
Graph. Model. | 1 |