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Abstract
In this paper we discuss the comparison of point cloud filters focusing on their applicability for streaming optimization. For the filtering stage within a stream pipeline processing we evaluate three filters: Voxel Grid, Pass Through and Statistical Outlier Removal. For the filters we perform series of the tests aiming at evaluation of changes of point cloud size and transmitting frequency (various fps ratio). We propose a distributed system used for point cloud processing and transferring them using computer network that fulfil requirements related to their effectiveness. We present results of the optimization process used for point cloud consolidation in the distributed environment. We show how to process the cloud of points before and do after the transmission. Both pre and post-processing allow a user to send the cloud via network without any delays, not causing the processing delay at the same time. Proposed pre-processing is focused on kind of compression of the cloud, and the post-processing on the reconstruction of it, so that the end-user application obtains the cloud with a given precision
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1515/pomr-2016-0025
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Published in:
-
Polish Maritime Research
pages 103 - 106,
ISSN: 1233-2585 - Title of issue:
- Proceedings of 1st International Conference on Innovative Research and Maritime Applications of Space Technology IRMAST 2015 strony 103 - 106
- Language:
- English
- Publication year:
- 2015
- Bibliographic description:
- Dziubich T., Szymański J., Brzeski A., Cychnerski J.: DEPTH IMAGES FILTERING IN DISTRIBUTED STREAMING// Proceedings of 1st International Conference on Innovative Research and Maritime Applications of Space Technology IRMAST 2015/ ed. Andrzej Stepnowski, Marek Banaszkiewicz, Marek Moszyński Gdańsk: Wydawnictwo Politechniki Gdańskiej, 2015, s.103-106
- DOI:
- Digital Object Identifier (open in new tab) 10.1515/pomr-2016-0025
- Verified by:
- Gdańsk University of Technology
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