A Novel Preprocessing Method for Dynamic Point-Cloud Compression
Source abstract
Abstract
Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponentially. This rapid increase in complexity has led to problems with recording and transmission. In this study, we propose a method of efficiently managing and compressing animation information stored in the 3D point-clouds sequence. A compressed point-cloud is created by reconfiguring the points based on their voxels. Compared with the original point-cloud, noise caused by errors is removed, and a preprocessing procedure that achieves high performance in a redundant processing algorithm is proposed. The results of experiments and rendering demonstrate an average file-size reduction of 40% using the proposed algorithm. Moreover, 13% of the over-lap data are extracted and removed, and the file size is further reduced.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Korea
Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
Ingenium College, Kwangwoon University, Seoul 01897, Korea
References (42)
Show source-provided references
1. (2021, June 25). Available online: https://www.microsoft.com/en-us/mixed-reality/capture-studios.
2. (2021, June 25). Available online: https://8i.com/.
3. (2021, June 25). 4D View Solutions, [Online]. Available online: http://www.4dviews.com.
4. High-quality streamable free-viewpoint video10.1145/2766945 ↗
5. Leroy, V., Franco, J.-S., and Boyer, E. (2017, January 22–29). Multi-view Dynamic Shape Refinement Using Local Temporal Integration. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.336 ↗
6. Multi-view Performance Capture of Surface Details10.1007/s11263-016-0979-1 ↗
7. Dynamic shape capture using multi-view photometric stereo10.1145/1618452.1618520 ↗
8. Graziosi, D., Nakagami, O., Kuma, S., Zaghetto, A., Suzuki, T., and Tabatabai, A. (2020). An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC). APSIPA Trans. Signal Inf. Process., 9.10.1017/ATSIP.2020.12 ↗
9. AR/MR Remote Collaboration on Physical Tasks: A Review10.1016/j.rcim.2020.102071 ↗
10. Cui, Y., Chen, R., Chu, W., Chen, L., Tian, D., Li, Y., and Cao, D. (2021). Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review. IEEE Trans. Intell. Transp. Syst.10.1109/TITS.2020.3023541 ↗
11. Schenk, T. (2005). Introduction to Photogrammetry, The Ohio State University.
12. Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling10.3390/s151127493 ↗
13. Rusu, R.B., and Cousins, S. (2011, January 9–13). 3d is here: Point cloud library (pcl). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980567 ↗
14. Accuracy analysis of kinect depth data
15. Chan, T.O., Lichti, D.D., Jahraus, A., Esfandiari, H., Lahamy, H., Steward, J., and Glanzer, M. (2018). An Egg Volume Measurement System Based on the Microsoft Kinect. Sensors, 18.10.3390/s18082454 ↗
16. Sun, G., and Wang, X. (2019). Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration. Agronomy, 9.10.3390/agronomy9100596 ↗
17. Robinson, M., and Parkinson, M. (2013, January 11–13). Estimating anthropometry with microsoft kinect. Proceedings of the 2nd International Digital Human Modeling Symposium, Ann Arbor, MI, USA.
18. Gupta, T., and Li, H. (2017, January 18–21). Indoor mapping for smart cities—An affordable approach: Using Kinect Sensor and ZED stereo camera. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.10.1109/IPIN.2017.8115909 ↗
19. Maksymova, I., Steger, C., and Druml, N. (2018). Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Proceedings, 2.10.3390/proceedings2130852 ↗
20. Jansson, S., Malmqvist, E., Mlacha, Y., Ignell, R., Okumu, F., Killeen, G., Kirkeby, C., and Brydegaard, M. (2021). Real-time dispersal of malaria vectors in rural Africa monitored with lidar. PLoS ONE, 16.10.1371/journal.pone.0247803 ↗
21. Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks10.1016/j.rse.2021.112322 ↗
22. Kim, H., and Lee, B. (August, January 31). Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.10.1109/ICRA40945.2020.9196663 ↗
23. Boyko, A.I., Matrosov, M.P., Oseledets, I.V., Tsetserukou, D., and Ferrer, G. (2020, January 25–29). TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition. Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.10.1109/IROS45743.2020.9341464 ↗
24. Cloud data modelling employing a unified, non-redundant triangular mesh
25. Reverse engineering physical models empolying wrap-aroud B-spline surfaces and quadrics10.1243/PIME_PROC_1996_210_100_02 ↗
26. Data reduction for reverse engineering
27. Surface simplification using a discrete curvature norm10.1016/S0097-8493(02)00121-8 ↗
28. Zhao, P., Wang, Y., and Hu, Q. (2016, January 10–15). A feature preserving algorithm for point cloud simplification based on hierarchical clustering. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.10.1109/IGARSS.2016.7730457 ↗
29. Data reduction in integrated reverse engineering and rapid prototyping10.1080/095119299130344 ↗
30. Point cloud simplification with preserved edge based on normal vector
31. Rockfall detection from terrestrial LiDAR point clouds: A clustering approach using R
32. 3D point cloud simplification algorithm based on fuzzy entropy iteration10.3788/AOS201333.0815001 ↗
33. Turk, G. (2001, January 12–17). Texture synthesis on surfaces. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques—SIGGRAPH ’01, Los Angeles, CA, USA.10.1145/383259.383297 ↗
34. (2021, June 20). MPEG 3DG, Call for Proposals for Point Cloud Compression v2. Available online: https://mpeg.chiariglione.org/standards/mpeg-i/point-cloud-compression/call-proposals-point-cloud-compression.
35. Zakharchenko, V. (2021, June 20). V-PCC Codec Description. Available online: https://mpeg.chiariglione.org/standards/mpeg-i/geometry-based-point-cloud-compression/g-pcc-codec-description-v2.
36. (2021, June 20). G-PCC Codec Description v2; ISO/IEC JTC1/SC29/WG11 N18189. Available online: https://committee.iso.org/home/jtc1sc29.
37. Series, H. (2003). Audiovisual and Multimedia Systems, Infrastructure of Audiovisual Services—Coding of Moving Video, Advanced Video Coding for Generic Audiovisual Services, International Telecommunication Union.
38. (2021, June 20). Coding, High Efficiency Video. Version 1, Rec.. Available online: https://www.itu.int/rec/T-REC-H.265.
39. Graziosi, D., Tabatabai, A., Zakharchenko, V., and Zaghetto, A. (2020, January 21–24). V-PCC Component Synchronization for Point Cloud Reconstruction. Proceedings of the 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland.10.1109/MMSP48831.2020.9287092 ↗
40. V-PCC Performance Evaluation of the First MPEG Point Codec10.5594/JMI.2021.3067962 ↗
41. (2021, May 27). MPEG Point Cloud Compression. Available online: https://mpeg-pcc.org/index.php/pcc-content-database/8i-voxelized-surface-light-field-8ivslf-dataset/.
42. Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., and Ranzuglia, G. (2008, January 2–4). Meshlab: An open-source mesh processing tool. Proceedings of the Eurographics Italian Chapter Conference, Salerno, Italy.