%0 Conference Paper %1 QMEX2020 %A Seufert, M. %A Kargl, J. %A Schauer, J. %A Nüchter, A. %A Hoßfeld, T. %B IEEE 12th International Conference on Quality of Multimedia Experience (QoMEX) %D 2020 %K author myown %P 1--6 %R 10.1109/QoMEX48832.2020.9123143 %T Different Points of View: Impact of 3D Point Cloud Reduction on QoE of Rendered Images %U https://robotik.informatik.uni-wuerzburg.de/telematics/download/qomex2020.pdf %X Modern photogrammetric methods as well as laser measurement systems make it easy to collect large 3D point clouds that sample objects or environments. As the recorded point clouds can be used to render computer-generated images and models, they are of particular interest in the domains of geographical and architectural engineering, as well as for computer graphics (e.g., games or virtual reality). However, point clouds have a huge storage demand, thus, point clouds shall be reduced by removing some of the points. This will inevitably also reduce the Quality of Experience (QoE) of media, which is rendered from the reduced point clouds. In this work, the impact of two different reduction methods on the QoE of rendered images is investigated from two point of views, i.e., based on ratings from both naive crowdworkers as well as point cloud experts.

@inproceedings{QMEX2020, abstract = { Modern photogrammetric methods as well as laser measurement systems make it easy to collect large 3D point clouds that sample objects or environments. As the recorded point clouds can be used to render computer-generated images and models, they are of particular interest in the domains of geographical and architectural engineering, as well as for computer graphics (e.g., games or virtual reality). However, point clouds have a huge storage demand, thus, point clouds shall be reduced by removing some of the points. This will inevitably also reduce the Quality of Experience (QoE) of media, which is rendered from the reduced point clouds. In this work, the impact of two different reduction methods on the QoE of rendered images is investigated from two point of views, i.e., based on ratings from both naive crowdworkers as well as point cloud experts. }, added-at = {2020-07-26T12:05:02.000+0200}, author = {{Seufert}, M. and {Kargl}, J. and {Schauer}, J. and {N{\"u}chter}, A. and {Hoßfeld}, T.}, biburl = {https://www.bibsonomy.org/bibtex/2805ee5061ee57fd3090cf4d4b3d6293f/nuechter76}, booktitle = {IEEE 12th International Conference on Quality of Multimedia Experience (QoMEX)}, doi = {10.1109/QoMEX48832.2020.9123143}, interhash = {bae2db2e4f4736b51acf6510cfdab4be}, intrahash = {805ee5061ee57fd3090cf4d4b3d6293f}, keywords = {author myown}, month = may, pages = {1--6}, timestamp = {2021-06-08T10:45:30.000+0200}, title = {Different Points of View: Impact of 3D Point Cloud Reduction on QoE of Rendered Images}, url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/qomex2020.pdf}, year = 2020 }