Interactions with Gigantic Point Clouds
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Date
2014-06-25
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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.
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