@INPROCEEDINGS{1438Skupin2014,
	AUTHOR = {Robert Skupin and Thilo Borgmann and Thomas Sikora},
	TITLE = {Multiview Point Cloud Filtering for Spatiotemporal Consistency},
	BOOKTITLE = {International Conference on Computer Vision Theory and Applications (VISAPP)},
	YEAR = {2014},
	MONTH = jan,
	PUBLISHER = {SCITEPRESS Digital Library},
	PAGES = {531--538},
	ORGANIZATION = {INSTICC},
	PDF = {http://elvera.nue.tu-berlin.de/files/1438Skupin2014.pdf},
	DOI = {10.5220/0004681805310538},
	URL = {http://elvera.nue.tu-berlin.de/files/1438Skupin2014.pdf},
	ABSTRACT = {This work presents algorithms to resample and filter point cloud data reconstructed from multiple cameras and multiple time instants. In an initial resampling stage, a voxel or a surface mesh based approach resamples the point cloud data into a common sampling grid. Subsequently, the resampled data undergoes a filtering stage based on clustering to remove artifacts and achieve spatiotemporal consistency across cameras and time instants. The presented algorithms are evaluated in a view synthesis scenario. Results show that view synthesis with enhanced depth maps as produced by the algorithms leads to less artifacts than synthesis with the original source data.}
}