Three-dimensional alpha shapes
Open accessAbstract
Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is sometimes useful or required to compute what one might call the “shape” of the set. For that purpose, this article introduces the formal notion of the family of α-shapes of a finite point set in R 3 . Each shape is a well-defined polytope, derived from the Delaunay triangulation of the point set, with a parameter α ε R controlling the desired level of detail. An algorithm is presented that constructs the entire family of shapes for a given set of size n in time 0(n 2 ) , worst case. A robust implementation of the algorithm is discussed, and several applications in the area of scientific computing are mentioned.
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The dimensions of the persistent homology groups that appear in the Homology Inference Theorem are efficiently computable using the persistence algorithm on the filtration of alpha complexes.
“Indeed, the dimensions of the persistent homology groups that appear in the theorem are efficiently computable using the persistence algorithm on the filtration of alpha complexes [11], [12].”
From Stability of Persistence Diagrams (2007) · cites this paper as [12]
Section 3.3, “Alpha Complexes” (page unavailable), defines alpha complexes and states that each is a subcomplex of those at larger alpha values, supporting the filtration part. “Features and Signatures” (page unavailable) describes an efficient way to track connected components, but calls higher-order homology signatures more challenging; it does not describe a persistence algorithm or establish efficient computation of the claimed persistent-homology dimensions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe Čech complex and topologically equivalent subcomplexes are delicate to compute because they rely on precise distances between points.
“However, the Čech complex and various topologically equivalent subcomplexes (e.g., the alpha complex of [13]) are delicate objects to compute, relying on the precise distances between the nodes in E n .”
From Barcodes: the persistent topology of data (2007) · cites this paper as [13]
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- Authors
- Edelsbrunner, H., & Mücke, E. P.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- ACM Transactions on Graphics
- Published
- 1994-01
- Volume
- 13
- Issue
- 1
- Pages
- 43-72
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2078290150
- Cited by
- 2,488
- References
- 38
- Citations by year
- 2026: 119 · 2025: 152 · 2024: 126 · 2023: 105 · 2022: 98 · 2021: 124 · 2020: 100 · 2019: 94 · 2018: 93 · 2017: 88 · 2016: 95 · 2015: 84 · 2014: 94 · 2013: 93 · 2012: 80
- FWCI
- 33.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Graphics (publishedVersion, public-domain)
- Funders
- National Science Foundation (9118874, 8921421)
- National Centre for Supercomputing Applications
- University of Illinois at Urbana-Champaign
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computational Geometry and Mesh Generation
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- alpha shapes, finite point sets, Delaunay triangulation, polytopes, shape recovery, level of detail, computational geometry
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