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src/plan.tex
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src/plan.tex
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\frontmatter{}
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\frontmatter{}
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%\input{introduction/main}
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\input{introduction/main}
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\resetstyle{}
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\resetstyle{}
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\mainmatter{}
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\mainmatter{}
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%\input{foreword/main}
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\input{foreword/main}
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\resetstyle{}
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\resetstyle{}
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%\input{state-of-the-art/main}
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\input{state-of-the-art/main}
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\resetstyle{}
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\resetstyle{}
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%\input{preliminary-work/main}
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\input{preliminary-work/main}
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\resetstyle{}
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\resetstyle{}
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%\input{dash-3d/main}
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\input{dash-3d/main}
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\resetstyle{}
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\resetstyle{}
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\input{system-bookmarks/main}
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\input{system-bookmarks/main}
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\backmatter{}
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\backmatter{}
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%\input{conclusion/main}
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\input{conclusion/main}
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@ -16,12 +16,23 @@ For these reasons, we decided to setup a new experiment, with a less complex tas
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\subsubsection{Models}
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\subsubsection{Models}
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In this user study, we display two successive 3D models to the users:
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In this user study, we display two successive 3D models to the users:
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\textbf{A FIGURE WOULD BE NICE !!!}
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\begin{itemize}
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\begin{itemize}
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\item For the tutorial phase, we use a model derived from a video game, representing a small scene, in order to prevent users from getting lost in the scene.
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\item For the tutorial phase, we use a model derived from a video game, representing a small scene, in order to prevent users from getting lost in the scene.
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\item For all the other parts of the experiment, we use a larger version of the Singaporean district 3D model, that include neighbouring districts such as Central Business District.
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\item For all the other parts of the experiment, we use a larger version of the Singaporean district 3D model, that include neighbouring districts such as Central Business District.
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\end{itemize}
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\end{itemize}
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\begin{figure}[ht]
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\begin{subfigure}[b]{0.395\textwidth}
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\includegraphics[width=\textwidth]{assets/system-bookmarks/models/before.png}
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\caption{Previous chapter model}
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\end{subfigure}
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\begin{subfigure}[b]{0.605\textwidth}
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\includegraphics[width=\textwidth]{assets/system-bookmarks/models/after.png}
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\caption{Extended model}
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\end{subfigure}
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\caption{Models used in our user studies}
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\end{figure}
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\subsubsection{Experiment}
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\subsubsection{Experiment}
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The experiment is articulated into four phases: a tutorial, a comparison between interfaces with and without bookmarks, a comparison between two streaming policies, and a final navigation during which the user is looking for objects in the scene.
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The experiment is articulated into four phases: a tutorial, a comparison between interfaces with and without bookmarks, a comparison between two streaming policies, and a final navigation during which the user is looking for objects in the scene.
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@ -111,6 +122,16 @@ On average, the difference in terms of PSNR is less obvious, and both strategies
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This may be due to the lower number of users clicking on bookmarks.
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This may be due to the lower number of users clicking on bookmarks.
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However, Figure~\ref{sb:psnr-third-experiment-after-click} is clear: the optimized policy performs way better after a click on a bookmark.
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However, Figure~\ref{sb:psnr-third-experiment-after-click} is clear: the optimized policy performs way better after a click on a bookmark.
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\begin{table}[th]
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\centering
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\begin{tabular}{ccccccccccc}
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\toprule \textbf{Policy} & \multicolumn{9}{c}{\textbf{Number of clicks}} & \textbf{Average} \\
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\midrule Greedy & 4 & 1 & 1 & 1 & 3 & 3 & 1 & 7 & 6 & \textbf{3}\\
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Bookmark & 3 & 5 & 2 & 5 & 10 & 7& 6& 4& 6 & \textbf{5.33}\\ \bottomrule
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\end{tabular}
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\caption{Number of click on bookmarks on the last experiment}
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\end{table}
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\begin{figure}[th]
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\begin{figure}[th]
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\centering
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\centering
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\begin{tikzpicture}
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\begin{tikzpicture}
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