The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Generalized delaunay triangulation for planar graphs
Lee, D. T., & Lin, A. K. (1986) · Discrete & Computational Geometry · doi:10.1007/bf02187695 · Cited by 286
Cited by
Quantitative diffusion measurements using the open-source software PyFRAP
Bläßle, A., Soh, G., Braun, T., Mörsdorf, D., Preiß, H., Jordan, B. M., & Müller, P. (2018) · Nature Communications · doi:10.1038/s41467-018-03975-6 · Cited by 49
Breast MRI and X-ray mammography registration using gradient values
García, E., Diez, Y., Diaz, O., Lladó, X., Gubern-Mérida, A., Martí, R., Martí, J., & Oliver, A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.013 · Cited by 26
Metadata
- Authors
- Si, H.
- Venue
- ACM Transactions on Mathematical Software
- Published
- 2015-02-04
- Volume
- 41
- Issue
- 2
- Pages
- 1-36
- ISSN
- 0098-3500 (ISSN-L)
- 1557-7295
- OpenAlex
- W2109991971
- Cited by
- 1,573
- References
- 1
- FWCI
- 411.82
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.