Paper Info
Title
On the design of optimisers for surface reconstruction
Abstract
In many industrial applications the need for an efficient and high-quality reconstruction of free-form surfaces does exist. Surface Reconstruction - the generation of CAD models from physical objects - has become an independent area of research. The supplementary modification and the automated manufacturing of workpieces represent typical fields of application. Small tolerances in the desired properties result in a very high number of scan points needed. Thus, modern approaches have to be capable of processing, analysing and modelling these amounts of data.There are several studies that use evolutionary algorithms (EA) for surface reconstruction tasks. Until now, these studies only describe the general ability of EA to successfully optimise surfaces. Aspects like runtime as well as comparability to other optimisation techniques have not been considered. Since these aspects are of great importance for integration in applicable software tools, in this paper the ability of a state-of-the-art multi-objective EA to be successfully integrated in surface reconstruction software is analysed. Major drawbacks are disclosed and necessary add-on modules are presented.
Year
DOI
Venue
2007
10.1145/1276958.1277379
GECCO
Keywords
Field
DocType
high-quality reconstruction,surface reconstruction,general ability,free-form surface,surface reconstruction task,applicable software tool,cad model,state-of-the-art multi-objective ea,surface reconstruction software,optimise surface,singular value decomposition,evolutionary algorithm,hybrid algorithm
CAD,Surface reconstruction,Singular value decomposition,Mathematical optimization,Computer aided geometric design,Evolutionary algorithm,Computer science,Software,Artificial intelligence,Comparability,Machine learning
Conference
Citations 
PageRank 
References 
8
0.50
14
Authors
3
Name
Order
Citations
PageRank
Tobias Wagner137517.02
Thomas Michelitsch2193.21
A. Sacharow3172.34