Updates

assets/preliminary-work/discovery.dat

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src/dash-3d/client.tex

@@ -341,7 +341,7 @@ The \texttt{DashLoader} class accepts as parameter a function that will be calle
 
 \subsubsection{Performance}
 
-Javascript requires the use of \emph{web workers} to perform parallel computing.
+JavaScript requires the use of \emph{web workers} to perform parallel computing.
 A web worker is a script in JavaScript that runs in the background, on a separate thread and that can communicate with the main script by sending and receiving messages.
 Since our system has many tasks to perform, it is natural to use workers to manage the streaming without impacting the framerate of the renderer.
 However, what a worker can do is very limited, since it cannot access the variables of the main script.

src/foreword/implementation.tex

@@ -13,7 +13,7 @@ When it comes to 3D streaming systems, we need two kind of software.
 \paragraph{THREE.js.}
 On the web browser, it is now possible to perform 3D rendering by using WebGL\@.
 However, WebGL is very low level and it can be painful to write code, even to render a simple triangle.
-For example, \href{https://www.tutorialspoint.com/webgl/webgl_drawing_a_triangle.htm}{this tutorial}'s code contains 121 lines of javascript, 46 being code (not comments or empty lines) to render a simple, non-textured triangle.
+For example, \href{https://www.tutorialspoint.com/webgl/webgl_drawing_a_triangle.htm}{this tutorial}'s code contains 121 lines of JavaScript, 46 being code (not comments or empty lines) to render a simple, non-textured triangle.
 For this reason, it seems unreasonable to build a system like the one we are describing in raw WebGL\@.
 There are many libraires that wrap WebGL code and that help people building 3D interfaces, and \href{https://threejs.org}{THREE.js} is a very popular one (56617 stars on github, making it the 35th most starred repository on GitHub as of November 26th, 2019\footnote{\url{https://web.archive.org/web/20191126151645/https://gitstar-ranking.com/mrdoob/three.js}}).
 THREE.js acts as a 3D engine built on WebGL\@.

src/introduction/outline.tex

@@ -10,7 +10,7 @@ The last section of this chapter focuses on 3D interaction.
 
 Then, in Chapter~\ref{bi}, we present our first contribution: an in-depth analysis of the impact of the UI on navigation and streaming in a 3D scene.
 We first develop a basic interface for navigating in 3D and then, we introduce 3D objects called \emph{bookmarks} that help users navigating in the scene.
-We then present a user study that we conducted on 50 people which shows that bookmarks ease user navigation: they improve performance at tasks such as finding objects.
+We then present a user study that we conducted on 51 people which shows that bookmarks ease user navigation: they improve performance at tasks such as finding objects.
 % Then, we setup a basic 3D streaming system that allows us to replay the traces collected during the user study and simulate 3D streaming at the same time.
 We analyze how the presence of bookmarks impacts the streaming: we propose and evaluate streaming policies based on precomputations relying on bookmarks and that measurably increase the quality of experience.
 

src/preliminary-work/bookmarks-impact.tex

@@ -54,7 +54,7 @@ Our NVE does not stream the 3D content for these experiments, in order to avoid
 \subsubsection{Task design}
 Since we are interested in studying how efficiently users navigate in the 3D scene, we ask our participants to complete a task which forces them to visit, at least partially, various regions in the scene.
 To this end, we hide a set of 8 coins on the scene: participants are asked to collect the coins by clicking on them.
-In order to avoid any bias due to the coins position, we predefined 50 possible coin locations all around the scene, and randomly select 8 out of these 50 positions each time a new participant starts the experiment.
+In order to avoid any bias due to the coins position, we predefined 50 possible coin locations per scene, and randomly select 8 out of these 50 positions each time a new participant starts the experiment.
 
 \subsubsection{Experiment}
 Participants are first presented with an initial screen to collect some preliminary information: age, gender, the last time they played 3D video games, and self-rated 3D gaming skills. We ask those questions because we believe that someone who is used to playing 3D video games should browse the scene more easily, and thus, may not need to use our bookmarks.
@@ -106,7 +106,7 @@ There were 51 participants (36 men and 15 women), who are in average 30.44 years
 We now present the results from our user study, focusing on whether bookmarks help users navigating the 3D scene.
 
 \subsubsection{Questionnaire}
-We had 51 responses to the Questionnaire.
+We had 51 responses to the questionnaire.
 The answers are summarized in Table~\ref{bi:questions}.
 Note that not all questions were answered by all participants.
 

src/preliminary-work/streaming.tex

@@ -19,7 +19,7 @@ Table~\ref{bi:modelsize} shows that material and texture amount at most for $3.6
 When a client starts loading the web page containing the 3D model, the server first sends the list of materials and the texture files.
 Then, the server periodically sends a fixed size chunk that indifferently encapsulates vertices, texture coordinates, or faces.
 A \textit{vertex} is coded with three floats and an integer ($x$, $y$, and $z$ coordinates and the index of the vertex), a \textit{texture coordinate} with two floats and an integer (the $x$ and $y$ coordinates on the image and the index of the texture coordinate), and a face with eight integers (the index of each vertex, the index of each texture coordinate, the index of the face and the number of the corresponding material).
-Consequently, given the Javascript implementation of integers and floats, we approximate each vertex and each texture coordinate to take up 32 bytes, and each face takes up 96 bytes.
+Consequently, given the JavaScript implementation of integers and floats, we approximate each vertex and each texture coordinate to take up 32 bytes, and each face takes up 96 bytes.
 
 \begin{table}[th]
     \centering
@@ -85,7 +85,7 @@ Computing this, however, requires rendering the scene at the server, and measuri
 It is not scalable to compute this for every viewpoint requested by the client.
 
 However, we can prerender the bookmarked viewpoints, since the number of bookmarks is limited, their viewpoints are known in advance, and they are likely to be accessed.
-For each bookmark, we render offline the scene using a single color per triangle.
+For each bookmark, we render the scene offline, using a single color per triangle.
 Once rendered, we scan the output image to find the visible triangles (based on the color) and sort them by decreasing projected area.
 This technique is also used by~\citep{view-dependent-progressive-mesh}.
 Thus, when the user clicks on a 3D bookmark, this precomputed list of faces is used by the server, and only visible faces are sent in decreasing order of contributions to the rendered image.