dash-3d-tmp/phd
2
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Adds self citation

Browse Source
This commit is contained in:
Thomas Forgione 2019-10-18 15:50:02 +02:00
parent 21679cea9f
commit 0467737c08
No known key found for this signature in database
GPG Key ID: 203DAEA747F48F41
2 changed files with 11 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
src/bib.bib
View File

@ -821,3 +821,12 @@
year={2019}, year={2019},
organization={ACM} organization={ACM}
} }
@inproceedings{cheng2008receiver,
title={Receiver-driven view-dependent streaming of progressive mesh},
author={Cheng, Wei and Ooi, Wei Tsang},
booktitle={Proceedings of the 18th International Workshop on Network and Operating Systems Support for Digital Audio and Video},
pages={9--14},
year={2008},
organization={ACM}
}

2
src/state-of-the-art/3d-streaming.tex
View File

@ -124,6 +124,8 @@ In terms of quality of experience, it is desirable that the downloaded content i
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 from the users point 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 from the users point of view.
This is typically called \emph{random accessible mesh compression}. This is typically called \emph{random accessible mesh compression}.
\citep{maglo2013pomar} is such an example of random accessible progressive 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.
Doing so reduces drastically 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, 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.