<div class="csl-bib-body">
<div class="csl-entry">Scheiblauer, C. (2014). <i>Interactions with gigantic point clouds</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2014.25458</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2014.25458
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/7754
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dc.description
Zusammenfassung in deutscher Sprache
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dc.description.abstract
During the last decade the increased use of laser range-scanners for sampling the environment has led to gigantic point cloud data sets. Due to the size of such data sets, tasks like viewing, editing, or presenting the data have become a challenge per se, as the point data is too large to fit completely into the main memory of a customary computer system. In order to accomplish these tasks and enable the interaction with gigantic point clouds on consumer grade computer systems, this thesis presents novel methods and data structures for efficiently dealing with point cloud data sets consisting of more than 109 point samples. To be able to access point samples fast that are stored on disk or in memory, they have to be spatially ordered, and for this a data structure is proposed which organizes the points samples in a level-of-detail hierarchy. Point samples stored in this hierarchy cannot only be rendered fast, but can also be edited, for example existing points can be deleted from the hierarchy or new points can be inserted. Furthermore, the data structure is memory efficient, as it only uses the point samples from the original data set. Therefore, the memory consumption of the point samples on disk, when stored in this data structure, is comparable to the original data set. A second data structure is proposed for selecting points. This data structure describes a volume inside which point samples are considered to be selected, and this has the advantage that the information about a selection does not have to be stored at the point samples. In addition to these two previously mentioned data structures, which represent novel contributions for point data visualization and manipulation, methods for supporting the presentation of point data sets are proposed. With these methods the user experience can be enhanced when navigating through the data. One possibility to do this is by using regional meshes that employ an out-of-core texturing method to show details in the mesoscopic scale on the surface of sampled objects, and which are displayed together with point clouds. Another possibility to increase the user experience is to use graphs in 3D space, which helps users to orient themselves inside point cloud models of large sites, where otherwise it would be difficult to find the places of interest. Furthermore, the quality of the displayed point cloud models can be increased by using a point size heuristics that can mimic a closed surface in areas that would otherwise appear undersampled, by utilizing the density of the rendered points in the different areas of the point cloud model. Finally, the use of point cloud models as a tool for archaeological work is proposed. Since it becomes increasingly common to document archaeologically interesting monuments with laser scanners, the number application areas of the resulting point clouds is raising as well. These include, but are not limited to, new views of the monument that are impossible when studying the monument on-site, creating cuts and floor plans, or perform virtual anastylosis. All these previously mentioned methods and data structures are implemented in a single software application that has been developed during the course of this thesis and can be used to interactively explore gigantic point clouds.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
point-based rendering
en
dc.subject
computer graphics
en
dc.title
Interactions with gigantic point clouds
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2014.25458
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Claus Scheiblauer
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E186 - Institut für Computergraphik und Algorithmen
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC11872151
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dc.description.numberOfPages
185
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-75220
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0002-9370-2663
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.openairetype
doctoral thesis
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item.grantfulltext
open
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crisitem.author.dept
E186 - Institut für Computergraphik und Algorithmen