Title: Evolutionary Generation of Primitive-Based Mesh Abstractions
Authors: Friedrich, Markus
Cuevas, Felip Guimerà
Sedlmeier, Andreas
Ebert, André
Citation: Journal of WSCG. 2018, vol. 26, no. 1, p. 17-26.
Issue Date: 2019
Publisher: Václav Skala - UNION Agency
Document type: článek
URI: http://hdl.handle.net/11025/35550
ISSN: 1213-6964 (on-line)
1213-6972 (print)
1213-6980 (CD-ROM)
Keywords: evoluční algoritmy;zpracování geometrie;Computer Aided Design;CSG;hluboké učení
Keywords in different language: evolutionary algorithms;geometry processing;CAD;CSG;deep learning
Abstract in different language: The procedural generation of data sets for empirical algorithm validation and deep learning tasks in the area of primitive-based geometry is cumbersome and time-consuming while ready-to-use data sets are rare. We propose a new and highly flexible framework based on Evolutionary Computing that is able to create primitive-based abstractions of existing triangle meshes favoring fast running times and high geometric variation over reconstruction precision. These abstractions are represented as CSG trees to widen the scope of possible applications. As part of the evaluation, we show how we successfully used the generator to create a data set for the evaluation of neural point cloud segmentation pipelines and additionally explain how to use the system to create artistic abstractions of meshes provided by publicly available triangle mesh databases.
Rights: © Václav Skala - UNION Agency
Appears in Collections:Volume 27, Number 1 (2019)

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