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Zishun Liu 0003

dblp:154/0219-3 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0002-6653-5142ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author

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
Geometric modeling and processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › computational geometry
barycentric coordinates
0.212014
Local barycentric coordinates · ACM Trans. Graph. 2014
Geometric modeling and processing
shape deformation
0.112014
Local barycentric coordinates · ACM Trans. Graph. 2014

Methods — techniques the papers use, named apart from their topics

total variation minimization · 0.2convex optimization · 0.2
YearPublicationVenuePosition
2016 Upright orientation of 3D shapes with Convolutional Networks
Zishun Liu 0003, Juyong Zhang, Ligang Liu 0001
Graph. Model.1
2014 Local barycentric coordinates
abstract
Barycentric coordinates yield a powerful and yet simple paradigm to interpolate data values on polyhedral domains. They represent interior points of the domain as an affine combination of a set of control points, defining an interpolation scheme for any function defined on a set of control points. Numerous barycentric coordinate schemes have been proposed satisfying a large variety of properties. However, they typically define interpolation as a combination ofallcontrol points. Thus alocalchange in the value at a single control point will create aglobalchange by propagation into the whole domain. In this context, we present a family oflocal barycentric coordinates(LBC), which select for each interior point a small set of control points and satisfy common requirements on barycentric coordinates, such as linearity, non-negativity, and smoothness. LBC are achieved through a convex optimization based on total variation, and provide a compact representation that reduces memory footprint and allows for fast deformations. Our experiments show that LBC provide more local and finer control on shape deformation than previous approaches, and lead to more intuitive deformation results.
Juyong Zhang, Bailin Deng, Zishun Liu 0003, Giuseppe Patanè 0001, Sofien Bouaziz, Kai Hormann, Ligang Liu 0001
ACM Trans. Graph.3