Název:	Evolutionary Generation of Primitive-Based Mesh Abstractions
Autoři:	Friedrich, Markus Cuevas, Felip Guimerà Sedlmeier, Andreas Ebert, André
Citace zdrojového dokumentu:	Journal of WSCG. 2018, vol. 26, no. 1, p. 17-26.
Datum vydání:	2019
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://hdl.handle.net/11025/35550
ISSN:	1213-6964 (on-line) 1213-6972 (print) 1213-6980 (CD-ROM)
Klíčová slova:	evoluční algoritmy;zpracování geometrie;Computer Aided Design;CSG;hluboké učení
Klíčová slova v dalším jazyce:	evolutionary algorithms;geometry processing;CAD;CSG;deep learning
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 27, Number 1 (2019)

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.