Depth Images Filtering In Distributed Streaming

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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.

1. W. Chau-Chang, C. Min-Shine: Nonmetric Camera Calibration for Underwater Laser Scanning System. IEEE Journal of Oceanic Engineering, vol. 05, 32(2), (2007), 383-399.

2. S. Ferrari, I Frosio, V. Piuri, N.A Borghese: Enhanced vector quantization for data reduction and filtering. Proceedings of 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT, (2004), 470–477.

3. S. Gernhardt, X. Cong, M. Eineder, S. Hinz, R. Bamler: Geometrical fusion of multitrack ps point clouds, IEEE Geoscience and Remote Sensing Letters, vol. 9(1), (2012), 38–42.

4. H. Haggag, M. Hossny, D. Filippidis, D. Creighton, S. Nahavandi, V. Puri: Measuring depth accuracy in rgbd cameras. 7th International Conference on Signal Processing and Communication Systems (ICSPCS), (2013), 1–7.

5. L. Hong Xie, Z. Zhao: A new method of cylinder reconstruction based on unorganized point cloud, 18th International Conference on Geoinformatics, (2010), 1–5.

6. P. Kiljański, Optimization of PCD consolidation process in distributed system, Master Thesis, Gdansk University of Technology, 2014

7. P. Li, H. Wang, Z. Liu: A morphological LIDAR point cloud filtering method based on fake scan lines, International Conference on Electronics, Communications and Control (ICECC), (2011), 1228–1231.

8. D. McLeod, J. Jacobson, M. Hardy, C. Embry: Autonomous inspection using an underwater 3D LiDAR. 2013 OCEANS, San Diego, (2013), 1-8.

9. S. Orts-Escolano, V. Morell, J. Garcia-Rodriguez, M. Cazorla: Point cloud data filtering and downsampling using growing neural gas. International Joint Conference on Neural Networks(IJCNN), (2013), 1–8.

10. R. Rusu, S. Cousins: 3D is here: Point cloud library (PCL). Proc. of International Conference in Robotics and Automation (ICRA), (2011), 1-4.

11. K. Santilli, K. Bemis, D. Silver, J. Dastur, P. Rona: Generating realistic images from hydrothermal plume data. Visualization, 2004. IEEE, (2004), 91-98

12. P. Thumbunpeng, M. Ruchanurucks, A Khongm: Surface area calculation using Kinect’s filtered point cloud with an application of burn care. International Conference on Robotics and Biomimetics (ROBIO), (2013), 2166–2169.

13. Y. Wan, Z. Miao, Z. Tang: Reconstruction of dense point cloud from uncalibrated widebaseline images. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), (2010), 1230–1233.

14. Y. Wang, X. Xiang Zhu, R. Bamler, S. Gernhardt: Towards terrasarx street view: Creating city point cloud from multiaspect data stacks. Proc. of Joint Urban Remote Sensing Event (JURSE), (2013), 198–201.

15. H. Wenming, L. Yuanwang, W. Peizhi, W. Xiaojun: Algorithm for 3d point cloud denoising. 3rd International Conference on Genetic and Evolutionary Computing WGEC ’09, (2009), 574–577

Polish Maritime Research

The Journal of Gdansk University of Technology

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IMPACT FACTOR 2017: 0.763
5-year IMPACT FACTOR: 0.816


CiteScore 2018: 1.48

SCImago Journal Rank (SJR) 2018: 0.391
Source Normalized Impact per Paper (SNIP) 2018: 1.141

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