<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related requirements. We discuss the comparison of point cloud filters focusing on their usage for streaming optimization. For the filtering step of the stream pipeline processing we evaluate four filters: Voxel Grid, Radial Outliner Remover, Statistical Outlier Removal and Pass Through. For each of the filters we perform a series of tests for evaluating the impact on the point cloud size and transmitting frequency (analysed for various fps ratio). We present results of the optimization process used for point cloud consolidation in a distributed environment. We describe the processing of the point clouds before and after the transmission. Pre- and post-processing allow the user to send the cloud via network without any delays. The proposed pre-processing compression of the cloud and the post-processing reconstruction of it are focused on assuring that the end-user application obtains the cloud with a given precision.</p></abstract>

In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related requirements. We discuss the comparison of point cloud filters focusing on their usage for streaming optimization. For the filtering step of the stream pipeline processing we evaluate four filters: Voxel Grid, Radial Outliner Remover, Statistical Outlier Removal and Pass Through. For each of the filters we perform a series of tests for evaluating the impact on the point cloud size and transmitting frequency (analysed for various fps ratio). We present results of the optimization process used for point cloud consolidation in a distributed environment. We describe the processing of the point clouds before and after the transmission. Pre- and post-processing allow the user to send the cloud via network without any delays. The proposed pre-processing compression of the cloud and the post-processing reconstruction of it are focused on assuring that the end-user application obtains the cloud with a given precision.

Depth Images Filtering In Distributed Streaming

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Depth Images Filtering In Distributed Streaming"}

In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related...

Depth Images Filtering In Distributed Streaming

Pubblicato online: 30 giu 2016

Pagine: 91 - 98

DOI: https://doi.org/10.1515/pomr-2016-0025

Parole chiave
point cloud processing, distributed system, parallel processing, depth image filtering

© 2016 Tomasz Dziubich et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Depth Images Filtering In Distributed Streaming

Pubblicato online: 30 giu 2016

Pagine: 91 - 98

DOI: https://doi.org/10.1515/pomr-2016-0025

Parole chiavepoint cloud processing, distributed system, parallel processing, depth image filtering

© 2016 Tomasz Dziubich et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
point cloud processing, distributed system, parallel processing, depth image filtering