\section{Contributions}

In this thesis, we have presented three main contributions.

\paragraph{}
First, we set up a basic system allowing 3D navigation and 3D content streaming.
We developped a navigation aid in the form of 3D bookmarks, and we conducted a user study to analyse its impact on navigation and streaming.
On one hand, we concluded that navigation aid help people navigating in a scene, they can perform tasks faster and more easily.
On the other hand, we showed that this help in 3D navigation comes at the cost of a negative impact for the quality of service: since users navigate faster, they require more data to perform in the same way.
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}.

\paragraph{}
Then, we put the focus on the streaming aspect of the system.
The objective of this contribution was to introduce a system able of performing view-dependent 3D streaming, without having the drawbacks of the basic system described in our first contribution (no support for texture, heavy computations on the server, no support for multi-resolution, no rendering performances considerations, \ldots).
We took massive inspiration from the DASH technology, that has become a standard for video streaming thanks to its solutions for all the issues described before.
We exploited the fact that DASH is made to be content agnostic to fit 3D content into its structure.
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}.

\paragraph{}
Finally, we brought back the 3D navigation aspect in DASH-3D.
We developped interfaces that allow navigating in 3D scenes for both desktop and mobile devices we reintroduced bookmarks in these interfaces.
The setup of our first contribution had some simplifications that made precomputations very easy to implement and efficient, particularly due to the fact that the server is able to know exactly what the client needs, and can thus create chunks adapted to the client's requirements.
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.
