Fast CSG Tree Reconstruction from Triangle Meshes via User-guided Partitioning.

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Title: Fast CSG Tree Reconstruction from Triangle Meshes via User-guided Partitioning.
Authors: Ströter, Daniel1 daniel_jan.stroeter@tu-darmstadt.de, Gonzalez-Nothnagel, Miguel1, Stegemann, Marcus1,2, Besler, Sebastian1,2, Mueller-Roemer, Johannes S.1,2, Friedrich, Markus3, Fayolle, Pierre-Alain4, Stork, André1,2
Source: Computer-Aided Design & Applications. 2026, Vol. 24 Issue 1, p36-54. 19p.
Subjects: Solid geometry, Genetic algorithms, Parallel algorithms, Geometric modeling
Abstract: The use of triangle meshes to represent geometric designs is ubiquitous in virtual prototyping. While geometric design is typically performed in computer-aided design (CAD) systems, triangle meshes usually do not allow for convenient shape modeling. Therefore, adjustment of the prototype's geometric design can be laborious and complex, if its CAD data is missing. To recover CAD data from a triangle mesh, we present a user-guided reconstruction method to obtain a constructive solid geometry (CSG) tree representing the geometry of the triangle mesh. In an interactive step, the user partitions identifiable design parts selecting mesh segments of common curvature. The user-guided partitioning allows for localized, per-partition reconstruction in a substantially reduced search space. To boost efficiency, we present GPU-accelerated primitive shape fitting as well as genetic evolution of CSG trees. As a result, our method enables fast CSG reconstruction improving run time performance by up to two orders of magnitude compared to state-of-the-art methods that require many minutes or even hours for complex reconstruction tasks. In addition, our method provides accurate reconstruction results reducing reconstruction errors by up to one order of magnitude compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of Computer-Aided Design & Applications is the property of Computer-Aided Design & Applications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22Computer-Aided+Design+%26+Applications%22">Computer-Aided Design & Applications</searchLink>. 2026, Vol. 24 Issue 1, p36-54. 19p.
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  Data: The use of triangle meshes to represent geometric designs is ubiquitous in virtual prototyping. While geometric design is typically performed in computer-aided design (CAD) systems, triangle meshes usually do not allow for convenient shape modeling. Therefore, adjustment of the prototype's geometric design can be laborious and complex, if its CAD data is missing. To recover CAD data from a triangle mesh, we present a user-guided reconstruction method to obtain a constructive solid geometry (CSG) tree representing the geometry of the triangle mesh. In an interactive step, the user partitions identifiable design parts selecting mesh segments of common curvature. The user-guided partitioning allows for localized, per-partition reconstruction in a substantially reduced search space. To boost efficiency, we present GPU-accelerated primitive shape fitting as well as genetic evolution of CSG trees. As a result, our method enables fast CSG reconstruction improving run time performance by up to two orders of magnitude compared to state-of-the-art methods that require many minutes or even hours for complex reconstruction tasks. In addition, our method provides accurate reconstruction results reducing reconstruction errors by up to one order of magnitude compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer-Aided Design & Applications is the property of Computer-Aided Design & Applications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.14733/cadaps.2027.36-54
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        Text: English
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      – SubjectFull: Genetic algorithms
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      – SubjectFull: Parallel algorithms
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              Text: 2026
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