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Thomas Forgione
2019-10-14 16:18:37 +02:00
parent 9029df1ec1
commit e7d9de164a
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@@ -10,7 +10,7 @@ On the other hand, we showed that this help in 3D navigation comes at the cost o
However, we also showed that this cost is not a fatality\todo{not sure of that sentence, we could also say \emph{is not inevitable}}.
Due to the prior knowledge we have about bookmarks, we are able to precompute data offline that we are then able to use when users click on bookmarks to improve the quality of service.
We then ran simulations on the traces we collected during the user study to show how these precomputations increase the quality of service.
This work has been published at the conference MMSys in 2016~\cite{bookmarks-impact}.
This work has been published at the conference MMSys in 2016~\citep{bookmarks-impact}.
\paragraph{}
Then, we put the focus on the streaming aspect of the system.
@@ -20,7 +20,7 @@ We exploited the fact that DASH is made to be content agnostic to fit 3D content
We used DASH-SRD extension to cut our 3D content into a $k$-d tree and profit from this structure to perform view-dependant streaming, without having any computation to run on the server side at all.
We implemented a few loading policies based on a utility metric that gives a score for each portion of the model.
We compared different values for a set of parameters, as well as our different loading policies by running simulations.
This work has been published at the conference ACMMM in 2018~\cite{dash-3d}. A demo paper was also published~\cite{dash-3d-demo}.
This work has been published at the conference ACMMM in 2018~\citep{dash-3d}. A demo paper was also published~\citep{dash-3d-demo}.
\paragraph{}
Finally, we brought back the 3D navigation aspect in DASH-3D.
@@ -29,4 +29,4 @@ The setup of our first contribution had some simplifications that made precomput
In DASH-3D, the data is structured and chunks are precomputed and do not depend on the client's need.
However, this does not mean that all hope is lost: we showed that we are still able to precompute an optimal order for chunks from each bookmark, and keep using the policies from the previous contribution, switching to this optimal order when a user clicks a bookmark.
We then ran simulations to show how the quality of service is impacted by those techniques.
A demo paper was published at the conference ACMMM in 2019~\cite{dash-3d-bookmarks-demo} showing the interfaces for desktop and mobile clients with bookmarks, but without any 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 any streaming aspect.