- pass on chapter 2

src/plan.tex

@@ -1,11 +1,11 @@
 \frontmatter{}
-\input{introduction/main}
+%\input{introduction/main}
 \mainmatter{}
-\input{foreword/main}
+%\input{foreword/main}
 \input{state-of-the-art/main}
-\input{preliminary-work/main}
+%\input{preliminary-work/main}
-\input{dash-3d/main}
+%\input{dash-3d/main}
-\input{system-bookmarks/main}
+%\input{system-bookmarks/main}
 \backmatter{}
-\input{conclusion/main}
+%\input{conclusion/main}
 

src/state-of-the-art/3d-interaction.tex

@@ -12,7 +12,7 @@ This is often known as point-of-interest (POI) movement (or \textit{go-to}, \tex
 Given such a point, the camera automatically moves from its current position to a new position that looks at the POI\@.
 One key issue of these techniques is to correctly orient the camera at destination.
 In Unicam \citep{two-pointer-input}, the so-called click-to-focus strategy automatically chooses the destination viewpoint depending on 3D orientations around the contact point.
-The recent Drag'n Go interaction \citep{drag-n-go} also hits a destination point while offering control on speed and position along the camera path.
+The more recent Drag'n Go interaction \citep{drag-n-go} also hits a destination point while offering control on speed and position along the camera path.
 This 3D interaction is designed in the screen space (it is typically a mouse-based camera control), where cursor's movements are mapped to camera movements following the same direction as the on-screen optical-flow.
 
 \begin{figure}[ht]

src/state-of-the-art/3d-streaming.tex

@@ -1,7 +1,7 @@
 \section{3D Streaming\label{sote:3d-streaming}}
 
-In this thesis, our objective is to stream 3D scenes.
+In this thesis, we focus on the objective of delivering large, massive 3D scenes over the network.
-Even though 3D streaming is not the most researched field, special attention has been brought to 3D content compression, in particular progressive compression which is a premise for 3D streaming.
+While 3D streaming is not the most standard field of research, there has been a special attention around 3D content compression, in particular progressive compression which can be considered a premise for 3D streaming.
 In the next sections, we review the 3D streaming related work, from 3D compression and structuring to 3D interaction.
 
 \subsection{Compression and structuring}
@@ -10,7 +10,7 @@ According to \citep{maglo20153d}, mesh compression can be divided into four cate
 \begin{itemize}
     \item single-rate mesh compression, seeking to reduce the size of a mesh;
     \item progressive mesh compression, encoding meshes in many levels of resolution that can be downloaded and rendered one after the other;
-    \item random accessible mesh compression, where parts of the models can be decoded in an arbitrary order;
+    \item random accessible mesh compression, where different parts of the models can be decoded in an arbitrary order;
     \item mesh sequence compression, compressing mesh animations.
 \end{itemize}
 
@@ -18,7 +18,7 @@ Since our objective is to stream 3D static scenes, single-rate mesh and mesh seq
 This section thus focuses on progressive meshes and random accessible mesh compression.
 
 Progressive meshes were introduced in~\citep{progressive-meshes} and allow a progressive transmission of a mesh by sending a low resolution mesh first, called \emph{base mesh}, and then transmitting detail information that a client can use to increase the resolution.
-To do so, an algorithm, called \emph{decimation algorithm}, starts from the original full resolution mesh and iteratively removes vertices and faces by merging vertices through the so called \emph{edge collapse} operation (Figure~\ref{sote:progressive-scheme}).
+To do so, an algorithm, called \emph{decimation algorithm}, starts from the original full resolution mesh and iteratively removes vertices and faces by merging vertices through the so-called \emph{edge collapse} operation (Figure~\ref{sote:progressive-scheme}).
 
 \begin{figure}[ht]
     \centering
@@ -75,8 +75,8 @@ To do so, an algorithm, called \emph{decimation algorithm}, starts from the orig
     \caption{Vertex split and edge collapse\label{sote:progressive-scheme}}
 \end{figure}
 
-Every time two vertices are merged, a vertex and two faces are removed from the original mesh, decreasing the resolution of the model.
+Every time two vertices are merged, a vertex and two faces are removed from the original mesh, decreasing the model resolution.
-After content preparation, the mesh consists in a base mesh and a sequence of partially ordered edge split operations.
+At the end of this content preparation phase, the mesh has been reorganized into a base mesh and a sequence of partially ordered edge split operations.
 Thus, a client can start by downloading the base mesh, display it to the user, and keep downloading refinement operations (vertex splits) and display details as time goes by.
 This process reduces the time a user has to wait before seeing a downloaded 3D object, thus increases the quality of experience.
 
@@ -86,7 +86,7 @@ This process reduces the time a user has to wait before seeing a downloaded 3D o
     \caption{Four levels of resolution of a mesh~\citep{progressive-meshes}}
 \end{figure}
 
-These methods have been vastly researched \citep{bayazit20093,mamou2010shape}, but very few of these methods can handle meshes with attributes, such as texture coordinates.
+%These methods have been vastly researched \citep{bayazit20093,mamou2010shape}, but very few of these methods can handle meshes with attributes, such as texture coordinates.
 
 \citep{streaming-compressed-webgl} develop a dedicated progressive compression algorithm based on iterative decimation, for efficient decoding, in order to be usable on web clients.
 With the same objective, \citep{pop-buffer} proposes pop buffer, a progressive compression method based on quantization that allows efficient decoding.
@@ -96,7 +96,7 @@ In \citep{batched-multi-triangulation}, the authors propose Nexus: a GPU optimiz
 It is notably used in 3DHOP (3D Heritage Online Presenter, \citep{3dhop}), a framework to easily build web interfaces to present 3D objects to users in the context of cultural heritage.
 
 Each of these approaches define its own compression and coding for a single mesh.
-However, users are frequently interested in scenes that contain many meshes, and the need to structure content emerged.
+However, users are often interested in scenes that contain multiple meshes, and the need to structure content emerged.
 
 To answer those issues, the Khronos group proposed a generic format called glTF (GL Transmission Format,~\citep{gltf}) to handle all types of 3D content representations: point clouds, meshes, animated model, etc.\
 glTF is based on a JSON file, which encodes the structure of a scene of 3D objects.
@@ -119,25 +119,25 @@ Their approach works well for several objects, but does not handle view-dependen
 \subsection{Viewpoint dependency}
 
 3D streaming means that content is downloaded while the user is interacting with the 3D object.
-In terms of quality of experience, it is desirable that the downloaded content is visible to the user.
+In terms of quality of experience, it is desirable that the downloaded content falls into the user's field of view.
-This means that the progressive compression must allow a decoder to choose what it needs to decode, and to guess what it needs to decode according to the users point of view.
+This means that the progressive compression must encode a spatial information in order to allow the decoder to determine content adapted to its viewpoint.
 This is typically called \emph{random accessible mesh compression}.
 \citep{maglo2013pomar} is such an example of random accessible progressive mesh compression.
-\citep{cheng2008receiver} proposes a receiver driven way of achieving viewpoint dependency with progressive mesh: the client starts by downloading the base mesh, and then is able to estimate the importance of vertex splits and choose which ones to download.
+\citep{cheng2008receiver} proposes a receiver driven way of achieving viewpoint dependency with progressive mesh: the client starts by downloading the base mesh, and from then is able to estimate the importance of the different vertex splits, in order to choose which ones to download.
-Doing so reduces drastically the server computational load, since it only has to send data, and improves the scalability of this framework.
+Doing so drastically reduces the server computational load, since it only has to send data, and improves the scalability of this framework.
 
-In the case of streaming a large 3D scene, viewpoint dependent streaming is a must-have: a user will only be seeing one small portion of the scene at each time, and a system that does not adapt its streaming to the user's point of view is bound to have poor quality of experience.
+In the case of streaming a large 3D scene, view-dependent streaming is fundamental: a user will only be seeing one small portion of the scene at each time, and a system that does not adapt its streaming to the user's point of view is bound to induce a low quality of experience.
 
-A simple way to implement viewpoint dependency is to access the content near the user's camera.
+A simple way to implement viewpoint dependency is to request the content that is spatially close to the user's camera.
 This approach, implemented in Second Life and several other NVEs (e.g.,~\citep{peer-texture-streaming}), only depends on the location of the avatar, not on its viewing direction.
 It exploits spatial coherence and works well for any continuous movement of the user, including turning.
 Once the set of objects that are likely to be accessed by the user is determined, the next question is in what order should these objects be retrieved.
 A simple approach is to retrieve the objects based on distance: the spatial distance from the user's virtual location and rotational distance from the user's view.
 
 More recently, Google integrated Google Earth 3D module into Google Maps.
-Users are now able to go to Google Maps, and click the 3D button which shifts the camera from the top-down view.
+Users are now able to go to Google Maps, and click the 3D button which shifts the camera from the aerial view.
-Even though there are no associated publications, it seems that the interface does view dependent streaming: low resolution from the center of the point of view gets downloaded right away, and then, data farther away or higher resolution data gets downloaded since it appears at a later time.
+Even though there are no associated publications to support this assertion, it seems clear that the streaming is view-dependent: low resolution from the center of the point of view gets downloaded first, and higher resolution data gets downloaded for closer objects than for distant ones.
-The choice of the nearby can be based based on an a priori, discretized, partitioned version of the environment; for example, \citep{3d-tiles} developed 3D Tiles, a specification for visualizing massive 3D geospatial data developed by Cesium and built on top of glTF\@.
+Determining the nearby content can be based on an a priori, discretized, partitioned version of the environment; for example, \citep{3d-tiles} developed 3D Tiles, a specification for visualizing massive 3D geospatial data developed by Cesium and built on top of glTF\@.
 Their main goal is to display 3D objects on top of regular maps.
 
 \begin{figure}[ht]
@@ -149,19 +149,19 @@ Their main goal is to display 3D objects on top of regular maps.
 \subsection{Geometry and textures}
 
 As discussed in Chapter~\ref{f:3d}, most 3D scenes consists in two main types of data: geometry and textures.
-When addressing 3D streaming, one must handle the concurrency between geometry and textures, and a system needs to solve this compromise.
+When addressing 3D streaming, one must handle the concurrency between geometry and textures, and the system needs to address this compromise.
 
 Balancing between streaming of geometry and texture data is addressed by~\citep{batex3},~\citep{visual-quality-assessment}, and~\citep{mesh-texture-multiplexing}.
 Their approaches combine the distortion caused by having lower resolution meshes and textures into a single view independent metric.
 \citep{progressive-compression-textured-meshes} also deals with the geometry / texture compromise.
 This work designs a cost driven framework for 3D data compression, both in terms of geometry and textures.
-This framework generates an atlas for textures that enables efficient compression and multiresolution scheme.
+Te authors generate an atlas for textures that enables efficient compression and multiresolution scheme.
 All four works considered a single, manifold textured mesh model with progressive meshes, and are not applicable in our work since we deal with large and potentially non-manifold scenes.
 
-Regarding texture streaming, \citep{simon2019streaming} propose a way to stream a set of textures by encoding the textures into a video.
+Regarding texture streaming, \citep{simon2019streaming} propose a way to stream a set of textures by encoding them into a video.
 Each texture is segmented into tiles of a fixed size.
 Those tiles are then ordered to minimise dissimilarities between consecutive tiles, and encoded as a video.
-By benefiting from the video compression techniques, they are able to reach a better rate-distortion ratio than webp, which is the new standard for texture transmission, and jpeg.
+By benefiting from the video compression techniques, the authors are able to reach a better rate-distortion ratio than webp, which is the new standard for texture transmission, and jpeg.
 However, the geometry / texture compromise is not the point of that paper.
 
 This thesis proposes a scalable streaming framework for large textured 3D scenes based on DASH, like~\citep{zampoglou}, but featuring a space partitioning of scenes in order to provide viewpoint dependent streaming.