| Attributes | Values |
|---|
| rdf:type
| |
| rdfs:seeAlso
| |
| Description
| - The fields of application of 3D building models are quite various such as 3D city models (visualizations, urban planning), intelligent transportation (smart navigation, augmented reality), environmental monitoring (propagation of road traffic noise, air pollution), special application (propagation of electromagnetic waves for telecommunication applications) and risk management (generation of flood maps). 3D building models have a great importance for specialists from a wide range of disciplines, therefore they are very important. The most common form of spatial data for the generation of 3D building models is a point cloud. Point cloud data represents the surface geometry of an object via independent distribution of points with uniform quality. A major influence on the generation of 3D building models is the density and quality of the point cloud, which is determined by scanning parameters (LiDAR principle) or ground sampling distances and overlaps between images (image matching techniques). The manual processing of point clouds is extremely time consuming and it is impossible to repeat it with the same result due to the human factor. Fully automatic methods of processing are used with increasing amounts of data that can be processed in shorter time periods. These methods of processing are very popular and in demand nowadays. There are several different commercial software products on the market that solve the problem of fully automatic generation of 3D building models. This paper will be a comparison of current commercial software products (ENVI LiDAR and INPHO Building Generator) that process this task. These software products have not yet been compared. Software testing will be performed on three datasets with different densities of point clouds.
- The fields of application of 3D building models are quite various such as 3D city models (visualizations, urban planning), intelligent transportation (smart navigation, augmented reality), environmental monitoring (propagation of road traffic noise, air pollution), special application (propagation of electromagnetic waves for telecommunication applications) and risk management (generation of flood maps). 3D building models have a great importance for specialists from a wide range of disciplines, therefore they are very important. The most common form of spatial data for the generation of 3D building models is a point cloud. Point cloud data represents the surface geometry of an object via independent distribution of points with uniform quality. A major influence on the generation of 3D building models is the density and quality of the point cloud, which is determined by scanning parameters (LiDAR principle) or ground sampling distances and overlaps between images (image matching techniques). The manual processing of point clouds is extremely time consuming and it is impossible to repeat it with the same result due to the human factor. Fully automatic methods of processing are used with increasing amounts of data that can be processed in shorter time periods. These methods of processing are very popular and in demand nowadays. There are several different commercial software products on the market that solve the problem of fully automatic generation of 3D building models. This paper will be a comparison of current commercial software products (ENVI LiDAR and INPHO Building Generator) that process this task. These software products have not yet been compared. Software testing will be performed on three datasets with different densities of point clouds. (en)
|
| Title
| - Comparison of Software Solutions for Automatic Generation of 3D Building Models
- Comparison of Software Solutions for Automatic Generation of 3D Building Models (en)
|
| skos:prefLabel
| - Comparison of Software Solutions for Automatic Generation of 3D Building Models
- Comparison of Software Solutions for Automatic Generation of 3D Building Models (en)
|
| skos:notation
| - RIV/68407700:21110/14:00220164!RIV15-MSM-21110___
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21110/14:00220164
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - airborne laser scanning; automatic generation; building models (en)
|
| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...v/mistoKonaniAkce
| |
| http://linked.open...i/riv/mistoVydani
| |
| http://linked.open...i/riv/nazevZdroje
| - 14th GeoConference on Informatics, Geoinformatics and Remote sensing - Conference Proceedings, Volume 1
|
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Halounová, Lena
- Kostin, Vitalii
- Hron, Vojtěch
|
| http://linked.open...vavai/riv/typAkce
| |
| http://linked.open.../riv/zahajeniAkce
| |
| issn
| |
| number of pages
| |
| http://bibframe.org/vocab/doi
| |
| http://purl.org/ne...btex#hasPublisher
| - International Multidisciplinary Scientific GeoConference SGEM
|
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
![[RDF Data]](/fct/images/sw-rdf-blue.png)
![[cxml]](/fct/images/cxml_doc.png)
![[csv]](/fct/images/csv_doc.png)
![[text]](/fct/images/ntriples_doc.png)
![[turtle]](/fct/images/n3turtle_doc.png)
![[ld+json]](/fct/images/jsonld_doc.png)
![[rdf+json]](/fct/images/json_doc.png)
![[rdf+xml]](/fct/images/xml_doc.png)
![[atom+xml]](/fct/images/atom_doc.png)
![[html]](/fct/images/html_doc.png)