Compression of 3D Geographical Objects at Various Level of Detail

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43929517" target="_blank" >RIV/49777513:23520/17:43929517 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-45123-7_26" target="_blank" >http://dx.doi.org/10.1007/978-3-319-45123-7_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-45123-7_26" target="_blank" >10.1007/978-3-319-45123-7_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Compression of 3D Geographical Objects at Various Level of Detail

Popis výsledku v původním jazyce

Compression of 3D objects has been recently discussed mainly in the domain of 3D computer graphics. However, more and more applications demonstrate that the third dimension plays an important role also in the domain of modelling and streaming of geographical objects. This is especially true for 3D city models and their distribution via internet. Despite the fact that compression of textual information related to geographical objects has a significant importance, in this paper we concentrate only on compression of geometry information and also on more complex geometries with irregular shapes. Considering the compression of 3D geographical objects, the 3D triangle meshes representation are used. 3D mesh compression is a way how to reduce the required cost of storage for triangle meshes without losing any details. The triangle is the basic geometric primitive for standard graphics rendering hardware. The compression algorithm aims at storing the input data into a binary file, that is as small as possible. For encoding of the mesh connectivity, our compression implements the popular EdgeBreaker algorithm. The character of geometry encoding is largely governed by the way connectivity is encoded. A popular choice of prediction for the EdgeBreaker algorithm is the parallelogram predictor. It has been observed in (Váša and Brunnett, 2013) that such prediction can be further improved by taking a two-step approach, first transmitting the complete connectivity and only afterwards transmitting the geometry. We used this approach to compress geographical objects at various level of detail. It does not bring an improvement for all datasets, especially meshes with many parallelogram shape prediction stencils do not benefit from it. However for complex geographical objects (bridges in our case) the used algorithm works nicely and after the compression the amount of data is even lower than 4% of the original file size.

Název v anglickém jazyce

Compression of 3D Geographical Objects at Various Level of Detail

Popis výsledku anglicky

Compression of 3D objects has been recently discussed mainly in the domain of 3D computer graphics. However, more and more applications demonstrate that the third dimension plays an important role also in the domain of modelling and streaming of geographical objects. This is especially true for 3D city models and their distribution via internet. Despite the fact that compression of textual information related to geographical objects has a significant importance, in this paper we concentrate only on compression of geometry information and also on more complex geometries with irregular shapes. Considering the compression of 3D geographical objects, the 3D triangle meshes representation are used. 3D mesh compression is a way how to reduce the required cost of storage for triangle meshes without losing any details. The triangle is the basic geometric primitive for standard graphics rendering hardware. The compression algorithm aims at storing the input data into a binary file, that is as small as possible. For encoding of the mesh connectivity, our compression implements the popular EdgeBreaker algorithm. The character of geometry encoding is largely governed by the way connectivity is encoded. A popular choice of prediction for the EdgeBreaker algorithm is the parallelogram predictor. It has been observed in (Váša and Brunnett, 2013) that such prediction can be further improved by taking a two-step approach, first transmitting the complete connectivity and only afterwards transmitting the geometry. We used this approach to compress geographical objects at various level of detail. It does not bring an improvement for all datasets, especially meshes with many parallelogram shape prediction stencils do not benefit from it. However for complex geographical objects (bridges in our case) the used algorithm works nicely and after the compression the amount of data is even lower than 4% of the original file size.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

The Rise of Big Spatial Data

ISBN

978-3-319-45122-0

ISSN

1863-2246

e-ISSN

1863-2351

Počet stran výsledku

14

Strana od-do

359-372

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Ostrava

Datum konání akce

16. 3. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000419321700026