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Thomas Forgione 2019-10-18 16:34:22 +02:00
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\frontmatter{} \frontmatter{}
%\input{introduction/main} \input{introduction/main}
\resetstyle{} \resetstyle{}
\mainmatter{} \mainmatter{}
%\input{foreword/main} \input{foreword/main}
\resetstyle{} \resetstyle{}
%\input{state-of-the-art/main} \input{state-of-the-art/main}
\resetstyle{} \resetstyle{}
%\input{preliminary-work/main} \input{preliminary-work/main}
\resetstyle{} \resetstyle{}
%\input{dash-3d/main} \input{dash-3d/main}
\resetstyle{} \resetstyle{}
\input{system-bookmarks/main} \input{system-bookmarks/main}
@ -22,5 +22,5 @@
\backmatter{} \backmatter{}
%\input{conclusion/main} \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
\subsubsection{Models} \subsubsection{Models}
In this user study, we display two successive 3D models to the users: In this user study, we display two successive 3D models to the users:
\textbf{A FIGURE WOULD BE NICE !!!}
\begin{itemize} \begin{itemize}
\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. \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.
\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. \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.
\end{itemize} \end{itemize}
\begin{figure}[ht]
\begin{subfigure}[b]{0.395\textwidth}
\includegraphics[width=\textwidth]{assets/system-bookmarks/models/before.png}
\caption{Previous chapter model}
\end{subfigure}
\begin{subfigure}[b]{0.605\textwidth}
\includegraphics[width=\textwidth]{assets/system-bookmarks/models/after.png}
\caption{Extended model}
\end{subfigure}
\caption{Models used in our user studies}
\end{figure}
\subsubsection{Experiment} \subsubsection{Experiment}
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. 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.
@ -61,7 +72,7 @@ Diamonds are hidden in the scene, and are invisible until the user is close enou
The users have to find the diamonds, and they can navigate by using indifferently the controls and the bookmarks. The users have to find the diamonds, and they can navigate by using indifferently the controls and the bookmarks.
The loading policy is the default greedy policy for half of the users, and the enhanced policy for bookmarks for the other half, and this order has been randomized. The loading policy is the default greedy policy for half of the users, and the enhanced policy for bookmarks for the other half, and this order has been randomized.
This is the most important part of the study, as we aim at observing several aspects. First, we hope that users navigate using the bookmarks. Since no guideline has been given to them as to how to interact, we want to observe whether they naturally use the bookmarks or not. This is the most important part of the study, as we aim at observing several aspects. First, we hope that users navigate using the bookmarks. Since no guideline has been given to them as to how to interact, we want to observe whether they naturally use the bookmarks or not.
In addition, we want to prove the superiority of our bookmark-optimized streaming policy by observing that users tend to perceive a better visual quality (as measured by the PSNR). In addition, we want to prove the superiority of our bookmark-optimized streaming policy by observing that users tend to perceive a better visual quality (as measured by the PSNR).
\subsubsection{Setup} \subsubsection{Setup}
@ -111,6 +122,16 @@ On average, the difference in terms of PSNR is less obvious, and both strategies
This may be due to the lower number of users clicking on bookmarks. This may be due to the lower number of users clicking on bookmarks.
However, Figure~\ref{sb:psnr-third-experiment-after-click} is clear: the optimized policy performs way better after a click on a bookmark. However, Figure~\ref{sb:psnr-third-experiment-after-click} is clear: the optimized policy performs way better after a click on a bookmark.
\begin{table}[th]
\centering
\begin{tabular}{ccccccccccc}
\toprule \textbf{Policy} & \multicolumn{9}{c}{\textbf{Number of clicks}} & \textbf{Average} \\
\midrule Greedy & 4 & 1 & 1 & 1 & 3 & 3 & 1 & 7 & 6 & \textbf{3}\\
Bookmark & 3 & 5 & 2 & 5 & 10 & 7& 6& 4& 6 & \textbf{5.33}\\ \bottomrule
\end{tabular}
\caption{Number of click on bookmarks on the last experiment}
\end{table}
\begin{figure}[th] \begin{figure}[th]
\centering \centering
\begin{tikzpicture} \begin{tikzpicture}