Evolutionary Generation of Primitive-Based Mesh Abstractions
| Autor: | Markus Friedrich, Andreas Sedlmeier, Felip Guimerà Cuevas, Andre Ebert |
|---|---|
| Přispěvatelé: | Skala, Václav |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Theoretical computer science
Computer science geometry processing 02 engineering and technology CSG 010501 environmental sciences computer.software_genre 01 natural sciences Evolutionary computation hluboké učení Triangle mesh 0202 electrical engineering electronic engineering information engineering Computer Aided Design Polygon mesh evoluční algoritmy CAD evolutionary algorithms 0105 earth and related environmental sciences ComputingMethodologies_COMPUTERGRAPHICS business.industry Deep learning zpracování geometrie deep learning 020207 software engineering Computer Graphics and Computer-Aided Design Data set Computational Mathematics Artificial intelligence business computer Software Scope (computer science) Generator (mathematics) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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