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1 parent 5da9181 commit d236e196765d28e066fde1698f5ccdbf51cc42a9 @naishh committed Mar 19, 2012
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  1. +35 −35 ch7_windowDetection.tex
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70 ch7_windowDetection.tex
@@ -41,53 +41,53 @@ \subsection{Introduction}
% digitale papers in map papers lezen
-Augment 3d models
- The more semantic information
- the more sophisticated and realistic a 3d model could be refined.
+This chapter is about one of the tasks of semantic urban scene interpretation, window detection.
+%explain what semantic 3d model is
+This semantic interpretation of urban scene's is used in a wide range of application domains.
+%which can be roughly divided in the next three.
- There are some virtual training applications that are designed for emergency response who requires interaction with a 3d model.
- Its importand to have a model that is of high visual quality and has sufficient detail
- This includes semantical information.
- Other applications that require semantic 3d models are urban planning, visual impact analysis and driving simulation.
+\paragraph{3d City models}
+ Manual creation of 3d models is a time consuming and expensive procedure.
+ Therefor semantic models are used for semi automatic 3d
+ reconstruction/modelling. Furthermore the semantic understanding is used
+ to increase the level of details in threedimensional city models which are
+ generated from aerial or satellite imagery. Some other applications can
+ automatically extract a CAD-like model of the building surface.\\
-position estimation
- building classification and detection
- used to identify a respective building
+\paragraph{Interactive 3d models}
+ There are some virtual training applications that are designed for
+ emergency response who requires interaction with a 3d model. For the
+ simulation to be realistic it is importand to have a model that is of high
+ visual quality and has sufficient semantic detail. Other applications that
+ require semantic 3d models are virtual tourism, urban planning, visual
+ impact analysis, driving simulation, military simulation and navigation
+ systems.\\
-semi automatic 3d reconstruction/modelling
- automatically recover a CAD-like model of the building surface
- semantic based (sparse?) city modeling
- create urban models
- citys
+\paragraph{Augmented reality}
+ Some mobile platforms apply augmented reality using facade and window
+ detection to make a accurate overlay of the building. An example overlay is
+ the same building but 200 years earlier. Semantical information is used to
+ not only indentify a respective building, but also find his exact location
+ in the image. An other application in this field that require semantic 3d
+ models is position estimation. This can also be combined with navigation
+ systems.
-augmented reality
- some mobile platforms use facade and window detection to make a accurate
- overlay of the building with that same building 200 years earlier.
-urban model refinement and visulisation
-augment 3d models
--facade interpretation,
--refine building facade models
-
-
-
-papers/75ShiPu.pdf
- For public security, accurate 3D building models are indis-
- pensable to make strategies during emergency situations. Navigation systems and
- virtual tourism also benefit from realistic city models.
Manual creation of city models is undoubtedly a rather time consuming and expen-
- sive procedure. On one hand, images are for long the only data source for geometric
+ sive procedure.
+ On one hand, images are for long the only data source for geometric
modelling, while recovering of 3D geometries is not straightforward from 2D im-
- ages. On the other hand, there are enormous amounts of objects (for example
+ ages.
+ On the other hand, there are enormous amounts of objects (for example
buildings) to be reconstructed, and their structures and shapes show a great vari-
- ety. There is a lack of automated approaches to understand the building structures
- captured by data. The rapid development of cities even adds to the cost of manual
- city model updating.
+ ety.
+ There is a lack of automated approaches to understand the building structures
+ captured by data.
@@ -122,7 +122,7 @@ \subsection{Introduction}
\subsubsection{Related work}
\emph{TODO}
-%todo
+%todo window detection related work
% tjoint article quoten

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