Merging faces: A new orthogonal simplification of solid models

Irving Cruz-Matías, Dolors Ayala

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new approach to simplify orthogonal pseudo-polyhedra (OPP) and binary volumes is presented. The method is incremental and produces a level-of-detail (LOD) sequence of OPP. Any object of this sequence contains the previous objects and, therefore, it is a bounding orthogonal approximation of them. The sequence finishes with the minimum axis-aligned bounding box (AABB). OPP are represented by the Extreme Vertices Model, a complete model that stores a subset of their vertices and performs fast Boolean operations. Simplification is achieved using a new approach called merging faces, which relies on the application of 2D Boolean operations. We also present a technique, based on the model continuity, for a better shape preservation. The method has been tested with several datasets and compared with two similar methods. © 2013 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationMerging faces: A new orthogonal simplification of solid models
Pages143-154
Number of pages12
ISBN (Electronic)9783642370663
DOIs
Publication statusPublished - 5 Apr 2013
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 5 Apr 2013 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7749 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period5/4/13 → …

Fingerprint

Solid Model
Merging
Simplification
Polyhedron
Face
Boolean Operation
Shape Preservation
Vertex Model
Simplify
Extremes
Binary
Subset
Approximation
Model
Object

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cruz-Matías, I., & Ayala, D. (2013). Merging faces: A new orthogonal simplification of solid models. In Merging faces: A new orthogonal simplification of solid models (pp. 143-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7749 LNCS). https://doi.org/10.1007/978-3-642-37067-0-13
Cruz-Matías, Irving ; Ayala, Dolors. / Merging faces: A new orthogonal simplification of solid models. Merging faces: A new orthogonal simplification of solid models. 2013. pp. 143-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Cruz-Matías, I & Ayala, D 2013, Merging faces: A new orthogonal simplification of solid models. in Merging faces: A new orthogonal simplification of solid models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7749 LNCS, pp. 143-154, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5/4/13. https://doi.org/10.1007/978-3-642-37067-0-13

Merging faces: A new orthogonal simplification of solid models. / Cruz-Matías, Irving; Ayala, Dolors.

Merging faces: A new orthogonal simplification of solid models. 2013. p. 143-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7749 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Cruz-Matías I, Ayala D. Merging faces: A new orthogonal simplification of solid models. In Merging faces: A new orthogonal simplification of solid models. 2013. p. 143-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-37067-0-13