Demo Data from EDBT 2026 / reviewers in the wild / expert
Yu Han Quek
dblp:299/0530
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Graphics, 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 |
Computational fabrication · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational fabrication › textile fabrication
machine knitting |
0.5 | 1 | 2021 | KnitKit: a flexible system for machine knitting of customizable textiles · ACM Trans. Graph. 2021 |
Methods — techniques the papers use, named apart from their topics
knitnet data structure · 0.5graph rewriting · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | KnitKit: a flexible system for machine knitting of customizable textilesabstractIn this work, we introduce KnitKit , a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns. Georges Nader, Yu Han Quek, Pei Zhi Chia, Oliver Weeger, Sai-Kit Yeung |
ACM Trans. Graph. | 2 |