Updates

src/introduction/challenges.tex

@@ -21,19 +21,19 @@ This opens multiple problems that we need to take care of.
 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, that it can render before downloading more chunks.
 Content preparation also includes compression.
-One of the question this thesis has to answer is \emph{what is the best way to prepare 3D content so that a client can benefit from it?}
+One of the questions this thesis has to answer is \emph{``what is the best way to prepare 3D content so that a client can benefit from it?''}
 
 \paragraph{Streaming policies.}
 Once our content is prepared and split in chunks, a client needs to determine which chunks it needs to download.
 A chunk that contains data in the field of view of the user is more relevant than a chunk outside of it; a chunk that is close to the camera is more relevant than a chunk far away from the camera, etc\ldots.
 This should also include other contextual parameters, such as the size of a chunk, the bandwidth, the user's behaviour, etc\ldots.
-The most important question we have to answer is \emph{how do we determine which chunks need to be downloaded depending on the chunks themselves and the user's interactions?}
+The most important question we have to answer is \emph{how to determine which chunks need to be downloaded depending on the chunks themselves and the user's interactions?}
 
 \paragraph{Evaluation.}
 In such systems, the two most important 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.
 The quality of experience is a user-centric metric, 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 can we compare streaming policies and evalute 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 evalute the impact of their parameters in terms of quality of service and quality of experience?}
 
 \paragraph{Implementation.}
 The objective of our work is to setup a client-server architecture that answers the problems mentioned earlier (content preparation, chunk utility, streaming policies).

src/introduction/main.tex

@@ -3,10 +3,10 @@
 \fresh{}
 
 During the last years, 3D acquisition and modeling techniques have progressed a lot.
-Recent software such as \href{https://alicevision.org/\#meshroom}{Meshroom} use \emph{structure from motion} and \emph{multi view stero} to infer a 3D model from a set of photographs.
+Recent software such as \href{https://alicevision.org/\#meshroom}{Meshroom} use \emph{structure from motion} and \emph{multi view stereo} to infer a 3D model from a set of photographs.
 There are more and more devices that are specifically built to obtain 3D data: some are more expensive and provide with very precise information such as Lidar, and some cheaper devices can obtain coarse data such as the Kinect.
-Thanks to these techniques, more and more 3D data becomes available.
-These models have potential for multiple purposes, for example, they can be 3D printed which can reduce the production cost of some pieces of hardware or enable the creation of new objects, but most uses will consist in visualisation.
+Thanks to these techniques, more and more 3D data become available.
+These models have potential for multiple purposes, for example, they can be 3D printed, which can reduce the production cost of some pieces of hardware or enable the creation of new objects, but most uses will consist in 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{Fitting Box} is a company that develops software to virtually try glasses).
 3D acquisition and visualisation is also useful to preserve cultural heritage, and software such as Google Heritage or 3DHop are such examples, or to allow users navigating in a city (as in Google Earth or Google Maps in 3D).
 \href{https://sketchfab.com}{Sketchfab} is an example of a website allowing users to share their 3D models and visualise models from other users.

src/introduction/outline.tex

@@ -9,7 +9,7 @@ Then it reviews the different manners of performing 3D streaming.
 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 we introduce 3D objects called \emph{bookmarks} that help users navigate in the scene.
+We first develop a basic interface for navigating in 3D and 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 have a great impact on how easy it is for a user to perform 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, and we propose and evaluate a few streaming policies that rely on pre-computations that can be made thanks to bookmarks and that can increase the quality of experience.
@@ -17,7 +17,7 @@ We analyse how the presence of bookmarks impacts the streaming, and we propose a
 In Chapter~\ref{d3}, we present the most important contribution of this thesis: DASH-3D.
 DASH-3D is an adaptation of 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 associates score to each chunk depending on the camera's position.
+We then define utility metrics that associate score to each chunk depending on the camera's position.
 Then, we present a client and various streaming policies based on our utilities that can benefit from the DASH format.
 We finally evaluate the different parameters of our client.