From 5e8d7dd4c26e9b67cef5f648f2fedb169b0e9595 Mon Sep 17 00:00:00 2001 From: acarlier Date: Sun, 20 Oct 2019 15:36:36 +0200 Subject: [PATCH] - conclusion contributions proofread --- src/conclusion/contributions.tex | 32 ++++++++++++++++---------------- src/plan.tex | 12 ++++++------ 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/src/conclusion/contributions.tex b/src/conclusion/contributions.tex index fb1f7e6..6011824 100644 --- a/src/conclusion/contributions.tex +++ b/src/conclusion/contributions.tex @@ -3,32 +3,32 @@ In this thesis, we have presented three main contributions. \paragraph{} -First, we set up a basic system allowing 3D navigation and 3D content streaming in the context of streaming online 3D content consisting in textured meshes. +First, we set up a basic system allowing navigation in a 3D scene (represented as a textured mesh) with the content being streamed through the network from a remote server. We developed a navigation aid in the form of \textbf{3D bookmarks}, and we conducted a user study to analyse its impact on navigation and streaming. On one hand, consistently with the state of the art, we observed that navigation aid \textbf{helps people navigating in a scene}, since they perform tasks faster and more easily. -On the other hand, we showed that benefiting from bookmarks in 3D navigation comes at the cost of a negative impact for the quality of service (QoS): since users navigate faster, they require more data during the same time span. -However, we also showed that this cost is not a fatality: using prior knowledge we have about bookmarks, we are able to \textbf{precompute data ordering offline} so that when users click on bookmark, their QoS improves. +On the other hand, we showed that benefiting from bookmarks in 3D navigation comes at the cost of a negative impact on the quality of service (QoS): since users navigate faster, they require more data during the same time span. +However, we also showed that this cost is not a fatality: using prior knowledge we have about bookmarks, we are able to \textbf{precompute an optimal data ordering offline} so that the QoS increases when users click on bookmarks. Simulations on the traces we collected during the user study quantify how these precomputations \textbf{improve the QoS}. -This work has been published at the conference MMSys in 2016~\citep{bookmarks-impact}. +This work has been published at the ACM MMSys conference in 2016~\citep{bookmarks-impact}. \paragraph{} -Then, we focus on the streaming aspect of the system. -The objective of this contribution is to introduce a system able to perform \textbf{scalable, view-dependent 3D streaming}. -This new framework proposes many improvements upon the basic system described in our first contribution: support for texture, moving computation from the server to the clients, support for multi-resolution textures, rendering performances considerations. -We took massive inspiration from the DASH technology, a standard for video streaming thanks to its scalability and its adaptability. +After studying the interactive aspect of 3D navigation, we proposed a contribution focusing on the streaming aspect of such a system. +The objective of this contribution wass to introduce a system able to perform \textbf{scalable, view-dependent 3D streaming}. +This new framework brings many improvements upon the basic system described in our first contribution: support for texture, externaliration of necessary computations from the server to the clients, support for multi-resolution textures, rendering performances considerations. +We drew massive inspiration from the DASH technology, a standard for video streaming used for its scalability and its adaptability. We exploit the fact that DASH is made to be content agnostic to fit 3D content into its structure. -We used DASH-SRD extension to partition our 3D content tree and profit from this partition to perform view-dependant streaming, without having any computation to run on the server side. -For the clients, we implement loading policies based on a utility metric that gives a score for both geometry and texture portion of the model. -We throughly tested our solutions for different values of parameters, as well as for our different loading policies by running simulations, to propose an efficient framework that we name DASH-3D. +Following the path set by DASH-SRD, we propose to tile 3D content using a tree and encode this partition into a description file (MPD) to allow view-dependent streaming, without the need for computation on the server side. +On the client side, we implement loading policies that optimize a utility metric estimating how much geometry and texture segments contribute to the visual rendering of the scene at a particular viewpoint. +We thoroughly tested our solutions by running simulations with different parameter values, as well as different loading policies, to propose an efficient framework that we name DASH-3D. This work has been published as a full paper at the conference ACMMM in 2018~\citep{dash-3d}. -A demo paper on the DASH-3D implementation was also published~\citep{dash-3d-demo}. +A demonstration paper on the DASH-3D implementation was also published~\citep{dash-3d-demo}. \paragraph{} -Finally, we brought back the \textbf{3D navigation bookmark within DASH-3D}. +Finally, we brought back the \textbf{3D navigation bookmark within our DASH-3D framework}. We developed interfaces that allow navigating in 3D scenes for both \textbf{desktop and mobile devices} and we reintroduced bookmarks in these interfaces. The setup of our first contribution considered only geometry, triangle by triangle, which made precomputations and ordering straightforward. -Moreover, the server knows exactly the client needs, and thus create chunks adapted to the client's requirements. +Moreover, as the server knew exactly the client needs, it could create chunks adapted to the client's requirements. In DASH-3D, the data is structured a priori (offline), so that chunks are grouped independently of a client's need. -However, this does not mean that all hope is lost: we are still able to precompute an optimal order for chunks from each bookmark, and, using the policies from our first contribution, switch to this optimal order when a user clicks a bookmark. +We therefore focus on precomputing an optimal order for chunks from each bookmark, and, alter the streaming policies from our second contribution to switch to this optimal order when a user clicks a bookmark. Simulations show that the QoS is positively impacted by those policies. -A demo paper was published at the conference ACMMM in 2019~\citep{dash-3d-bookmarks-demo} showing the interfaces for desktop and mobile clients with bookmarks, but without the streaming aspect. +A demo paper was published at the conference ACMMM in 2019~\citep{dash-3d-bookmarks-demo} showing the interfaces for desktop and mobile clients with bookmarks, but without the streaming aspect. A journal paper will be submitted shortly to value this third contribution. diff --git a/src/plan.tex b/src/plan.tex index 826ce84..eeb7000 100644 --- a/src/plan.tex +++ b/src/plan.tex @@ -1,23 +1,23 @@ \frontmatter{} -\input{introduction/main} +%\input{introduction/main} \resetstyle{} \mainmatter{} -\input{foreword/main} +%\input{foreword/main} \resetstyle{} -\input{state-of-the-art/main} +%\input{state-of-the-art/main} \resetstyle{} -\input{preliminary-work/main} +%\input{preliminary-work/main} \resetstyle{} -\input{dash-3d/main} +%\input{dash-3d/main} \resetstyle{} -\input{system-bookmarks/main} +%\input{system-bookmarks/main} \resetstyle{} \backmatter{}