Memory Efficient Surface Reconstruction Based on Self Organising Maps
| dc.contributor.author | Kaye, David Paul | en_US |
| dc.contributor.author | Ivrissimtzis, Ioannis | en_US |
| dc.contributor.editor | Ian Grimstead and Hamish Carr | en_US |
| dc.date.accessioned | 2013-10-31T10:30:52Z | |
| dc.date.available | 2013-10-31T10:30:52Z | |
| dc.date.issued | 2011 | en_US |
| dc.description.abstract | We propose a memory efficient, scalable surface reconstruction algorithm based on self organising maps (SOMs). Following previous approaches to SOM based implicit surface reconstruction, the proposed SOM has the geometry of a regular grid and is trained with point samples extracted along the normals of the input data. The layer by layer training of the SOM makes the algorithm memory efficient and scalable as at no stage there is need to hold the entire SOM in memory. Experiments show that the proposed algorithm can support the training of the very large SOMs that are needed for richly detailed surface reconstructions. | en_US |
| dc.description.seriesinformation | Theory and Practice of Computer Graphics | en_US |
| dc.identifier.isbn | 978-3-905673-83-8 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG11/025-032 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms, languages, and systems | en_US |
| dc.title | Memory Efficient Surface Reconstruction Based on Self Organising Maps | en_US |