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src/introduction/challenges.tex

@@ -1,39 +1,39 @@
 \section{Open problems\label{i:challenges}}
 
-The objective of our work is to design a system that allows a user to access remote 3D content and that guarantees both good quality of service and good quality of experience.
+The objective of our work is to design a system which allows a user to access remote 3D content \update{and that guarantees both good quality of service and good quality of experience}{}.
 A 3D streaming client has lots of tasks to accomplish:
 
 \begin{itemize}
-    \item decide what part of the model to download next;
-    \item download the next part;
-    \item parse the downloaded content;
-    \item add the parsed result to the scene;
-    \item render the scene;
-    \item manage the interaction with the user.
+    \item Decide what part of the \update{model}{content} to download next,
+    \item Download the next part,
+    \item Parse the downloaded content,
+    \item Add the parsed result to the scene,
+    \item Render the scene,
+    \item Manage the interaction with the user.
 \end{itemize}
 
-This opens multiple problems that need to be considered and will be studied in this thesis.
+This opens multiple problems which need to be considered and will be studied in this thesis.
 
 \paragraph{Content preparation.}
 % Any preprocessing that can be done on our 3D data gives us a strategical advantage since it consists in computations that will not be needed live, neither for the server nor for the client.
 % Furthermore, for streaming, data needs to be split into chunks that are requested separately, so perparing those chunks in advance can also help the streaming.
 Before streaming content, it needs to be prepared.
 The segmentation of the content into chunks is particularly important for streaming since it allows transmitting only a portion of the data to the client.
-A partial model consisting in the downloaded content, it can be rendered while downloading more chunks.
+\update{A partial model consisting in the downloaded content, it}{The downloaded chunks} can be rendered while \update{downloading more chunks}{more chunks are being downloaded}.
 Content preparation also includes compression.
 One of the questions this thesis has to answer is: \emph{what is the best way to prepare 3D content so that a streaming client can progressively download and render the 3D model?}
 
 \paragraph{Streaming policies.}
 Once our content is prepared and split in chunks, a client needs to determine which chunks should be downloaded first.
-A chunk that contains data in the field of view of the user is more relevant than a chunk that is not inside; a chunk that is close to the camera is more relevant than a chunk far away from the camera, etc.
-This should also include other contextual parameters, such as the size of a chunk, the bandwidth, the user's behaviour, etc.
-The most important questions we have to answer are: \emph{how to estimate a chunk utility, and how to determine which chunks need to be downloaded depending the user's interactions?}
+A chunk that contains data in the field of view of the user is more relevant than a chunk that is not inside; a chunk that is close to the camera is more relevant than a chunk far away from the camera.
+This should also include other contextual parameters, such as the size of a chunk, the bandwidth and the user's behaviour.
+\update{The most important questions we have to answer are:}{In order to propose efficient streaming policies, we need to know} \emph{how to estimate a chunk utility, and how to determine which chunks need to be downloaded depending the user's interactions?}
 
 \paragraph{Evaluation.}
-In such systems, the two most important criteria for evaluation are quality of service, and quality of experience.
+In such systems, \update{the}{} two \update{most important}{commonly used} criteria for evaluation are quality of service, and quality of experience.
 The quality of service is a network-centric metric, which considers values such as throughput and measures how well the content is served to the client.
 The quality of experience is a user-centric metric: it relies on user perception and can only be measured by asking how users feel about a system.
-To be able to know which streaming policies are best, one needs to know \emph{how to compare streaming policies and evaluate the impact of their parameters in terms of quality of service and quality of experience?}
+To be able to know which streaming policies are best, one needs to know \emph{how to compare streaming policies and evaluate the impact of their parameters \update{in terms of}{on the} quality of service \update{}{of the streaming system} and \update{}{on the} quality of experience \update{}{of the final user}?}
 
 \paragraph{Implementation.}
 The objective of our work is to setup a client-server architecture that answers the above problems: content preparation, chunk utility, streaming policies.

src/introduction/main.tex

@@ -1,8 +1,8 @@
 \chapter{Introduction\label{i}}
 
 During the last years, 3D acquisition and modeling techniques have made tremendous progress.
-Recent software use 2D images from cameras to reconstruct 3D data, e.g. \href{https://alicevision.org/\#meshroom}{Meshroom} is free and open source software that got almost \numprint{200000} downloads on \href{https://www.fosshub.com/Meshroom.html}{fosshub}, that use \emph{structure-from-motion} and \emph{multi-view-stereo} to infer a 3D model.
-There are more and more devices that are specifically built to harvest 3D data: some still very expensive and provide precise information such as LIDAR (Light Detection And Ranging, as in RADAR but with light instead of radio waves), while some cheaper devices can obtain coarse data such as the Kinect.
+Recent software uses 2D images from cameras to reconstruct 3D data, e.g. \href{https://alicevision.org/\#meshroom}{Meshroom} is a free and open source software which got almost \numprint{200000} downloads on \href{https://www.fosshub.com/Meshroom.html}{fosshub}, which use \emph{structure-from-motion} and \emph{multi-view-stereo} to infer a 3D model.
+More and more devices are specifically built to harvest 3D data: \update{some still very expensive and provide precise information such as LIDAR (Light Detection And Ranging, as in RADAR but with light instead of radio waves), while some cheaper devices can obtain coarse data such as the Kinect.}{for example, LIDAR (Light Detection And Ranging) can compute 3D distances by measuring time of flight of light. The recent research interest for autonomous vehicles allowed more companies to develop cheaper LIDARs, which increase the potential for new 3D content creation.}
 Thanks to these techniques, more and more 3D data become available.
 These models have potential for multiple purposes, for example, they can be printed, which can reduce the production cost of some pieces of hardware or enable the creation of new objects, but most uses are based on visualisation.
 For example, they can be used for augmented reality, to provide user with feedback that can be useful to help worker with complex tasks, but also for fashion (for example, \emph{Fittingbox} is a company that develops software to virtually try glasses, as in Figure~\ref{i:fittingbox}).

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 that 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 50 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 analyse how the presence of bookmarks impacts the streaming: we propose and evaluate streaming policies based on pre-computations relying on bookmarks and that measurably increase the quality of experience.
 
@@ -18,7 +18,7 @@ In Chapter~\ref{d3}, we present the most important contribution of this thesis:
 DASH-3D is an adaptation of DASH (Dynamic Adaptive Streaming over HTTP): the video streaming standard, to 3D streaming.
 We first describe how we adapt the concepts of DASH to 3D content, including the segmentation of content.
 We then define utility metrics that associate score to each chunk depending on the user's position.
-Then, we present a client and various streaming policies based on our utilities that can benefit from DASH format.
+Then, we present a client and various streaming policies based on our utilities which can benefit from DASH format.
 We finally evaluate the different parameters of our client.
 
 In Chapter~\ref{sb}, we present our last contribution: the integration of the interaction ideas that we developed in Chapter~\ref{bi} into DASH-3D.