Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
EDBT 2026 Demo / reviewers in the wild / expert

Hang Si

dblp:40/6015 · DBLP profile ↗
← Back
8ranked-venue papers
3as first author
0since 2021 · last 2020
0009-0006-1096-1050ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 6 · 2 first-authorTheory of computation · 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.

Theoretical computer science
3 papers
Computational geometry · 100%
Computer graphics and multimedia
3 papers
Geometric modeling and processing · 100%

Topics — the 13 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational geometry
steiner points		 0.722018
Generalised Bagemihl polyhedra and a tight bound on the number of interior Steiner points · Comput. Aided Des. 2018
On tetrahedralisations of generalised Chazelle polyhedra with interior Steiner points · Comput. Aided Des. 2018
Geometric modeling and processing › mesh processing
remeshing		 0.412019
Curvature adaptive surface remeshing by sampling normal cycle · Comput. Aided Des. 2019
Geometric modeling and processing › mesh processing › mesh optimization
mesh quality improvement		 0.312018
Tetrahedral mesh improvement using moving mesh smoothing, lazy searching flips, and RBF surface reconstruction · Comput. Aided Des. 2018
Geometric modeling and processing › mesh generation
tetrahedral meshing		 0.312018
Tetrahedral mesh improvement using moving mesh smoothing, lazy searching flips, and RBF surface reconstruction · Comput. Aided Des. 2018
Computational geometry › polytopes
polyhedra		 0.312018
Generalised Bagemihl polyhedra and a tight bound on the number of interior Steiner points · Comput. Aided Des. 2018
Computational geometry › mesh generation
tetrahedralization		 0.312018
On tetrahedralisations of generalised Chazelle polyhedra with interior Steiner points · Comput. Aided Des. 2018
Geometric modeling and processing › mesh processing › mesh adaptation
anisotropic mesh adaptation		 0.312017
A priori anisotropic mesh adaptation driven by a higher dimensional embedding · Comput. Aided Des. 2017
Geometric modeling and processing › mesh processing
mesh adaptation		 0.312017
A priori anisotropic mesh adaptation driven by a higher dimensional embedding · Comput. Aided Des. 2017
Computational geometry › triangulation › delaunay triangulation
constrained delaunay triangulation		 0.212014
Higher-Quality Tetrahedral Mesh Generation for Domains with Small Angles by Constrained Delaunay Refinement · SoCG 2014
Computational geometry › mesh generation
delaunay refinement		 0.212014
Higher-Quality Tetrahedral Mesh Generation for Domains with Small Angles by Constrained Delaunay Refinement · SoCG 2014
Computational geometry
mesh generation		 0.212014
Higher-Quality Tetrahedral Mesh Generation for Domains with Small Angles by Constrained Delaunay Refinement · SoCG 2014
Geometric modeling and processing › surface reconstruction › implicit surface reconstruction
radial basis function surface reconstruction		 0.112018
Tetrahedral mesh improvement using moving mesh smoothing, lazy searching flips, and RBF surface reconstruction · Comput. Aided Des. 2018
Geometric modeling and processing
surface reconstruction		 0.112018
Tetrahedral mesh improvement using moving mesh smoothing, lazy searching flips, and RBF surface reconstruction · Comput. Aided Des. 2018

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

normal cycle sampling · 0.4moving mesh smoothing · 0.3lazy searching flips · 0.3RBF surface reconstruction · 0.3higher dimensional embedding · 0.3constrained delaunay refinement · 0.2
YearPublicationVenuePosition
2020 Parallel hybrid mesh adaptation by refinement and coarsening
Pengcheng Cui, Yaobin Zhang, Hang Si
 Graph. Model.5
2019 Curvature adaptive surface remeshing by sampling normal cycle
Kehua Su, Na Lei, Wei Chen 0130, Hang Si, Shikui Chen, Xianfeng Gu
 Comput. Aided Des.5
2018 Tetrahedral mesh improvement using moving mesh smoothing, lazy searching flips, and RBF surface reconstruction
Franco Dassi, Lennard Kamenski, Patricio Farrell, Hang Si
 Comput. Aided Des.4
2018 On tetrahedralisations of generalised Chazelle polyhedra with interior Steiner points
Hang Si, Nadja Goerigk
 Comput. Aided Des.1
2018 Generalised Bagemihl polyhedra and a tight bound on the number of interior Steiner points
Hang Si, Nadja Goerigk
 Comput. Aided Des.1
2017 A priori anisotropic mesh adaptation driven by a higher dimensional embedding
Franco Dassi, Simona Perotto, Hang Si, Timo Streckenbach
 Comput. Aided Des.3
2015 TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator
abstract
TetGen is a C++ program for generating good quality tetrahedral meshes aimed to support numerical methods and scientific computing. The problem of quality tetrahedral mesh generation is challenged by many theoretical and practical issues. TetGen uses Delaunay-based algorithms which have theoretical guarantee of correctness. It can robustly handle arbitrary complex 3D geometries and is fast in practice. The source code of TetGen is freely available. This article presents the essential algorithms and techniques used to develop TetGen. The intended audience are researchers or developers in mesh generation or other related areas. It describes the key software components of TetGen, including an efficient tetrahedral mesh data structure, a set of enhanced local mesh operations (combination of flips and edge removal), and filtered exact geometric predicates. The essential algorithms include incremental Delaunay algorithms for inserting vertices, constrained Delaunay algorithms for inserting constraints (edges and triangles), a new edge recovery algorithm for recovering constraints, and a new constrained Delaunay refinement algorithm for adaptive quality tetrahedral mesh generation. Experimental examples as well as comparisons with other softwares are presented.
Hang Si
 ACM Trans. Math. Softw.1
2014 Higher-Quality Tetrahedral Mesh Generation for Domains with Small Angles by Constrained Delaunay Refinement
abstract
Most algorithms for guaranteed-quality tetrahedral mesh generation create Delaunay meshes. Delaunay triangulations have many good properties, but the requirement that all tetrahedra be Delaunay often forces mesh generators to overrefine where boundary polygons meet at small angles---that is, they produce too many tetrahedra, making them too small. Relaxing the Delaunay property makes it possible both to reduce overrefinement and to obtain higher-quality tetrahedra.
Jonathan Richard Shewchuk, Hang Si
 SoCG2