{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:40:09Z","timestamp":1760236809051,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265\/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.<\/jats:p>","DOI":"10.3390\/s22010197","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T02:31:27Z","timestamp":1640745087000},"page":"197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0262-5595","authenticated-orcid":false,"given":"Emil","family":"Dumic","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University North, 104. Brigade 3, 42000 Vara\u017edin, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4795-9510","authenticated-orcid":false,"given":"Anamaria","family":"Bjelopera","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computing, University of Dubrovnik, Cira Carica 4, 20000 Dubrovnik, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3870-783X","authenticated-orcid":false,"given":"Andreas","family":"N\u00fcchter","sequence":"additional","affiliation":[{"name":"Department of Informatics VII\u2014Robotics and Telematics, Julius-Maximilians-University W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"JPEG Pleno: Standardizing a Coding Framework and Tools for Plenoptic Imaging Modalities","volume":"3","author":"Astola","year":"2020","journal-title":"ITU J. ICT Discov."},{"key":"ref_2","unstructured":"Perkis, A., Timmerer, C., Barakovi\u0107, S., Husi\u0107, J.B., Bech, S., Bosse, S., Botev, J., Brunnstr\u00f6m, K., Cruz, L., and Moor, K.D. (2020, January 25). QUALINET White Paper on Definitions of Immersive Media Experience (IMEx). Proceedings of the European Network on Quality of Experience in Multimedia Systems and Services, 14th QUALINET Meeting, Online."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11831-019-09320-4","article-title":"Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications","volume":"27","author":"Wang","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_4","first-page":"75","article-title":"Chapter 2\u2014Plenoptic imaging: Representation and processing","volume":"Volume 6","author":"Chellappa","year":"2018","journal-title":"Academic Press Library in Signal Processing"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"van der Hooft, J., Vega, M.T., Timmerer, C., Begen, A.C., De Turck, F., and Schatz, R. (2020, January 26\u201328). Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming. Proceedings of the 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), Athlone, Ireland.","DOI":"10.1109\/QoMEX48832.2020.9123081"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Han, B., Liu, Y., and Qian, F. (2020, January 21\u201325). ViVo: Visibility-aware mobile volumetric video streaming. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020, London, UK.","DOI":"10.1145\/3372224.3380888"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dumic, E., Battisti, F., Carli, M., and da Silva Cruz, L.A. (2020, January 18\u201321). Point Cloud Visualization Methods: A Study on Subjective Preferences. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287504"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TMM.2020.3037481","article-title":"Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality","volume":"23","author":"Javaheri","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dumic, E., Bjelopera, A., and N\u00fcchter, A. (2019, January 9\u201312). Projection based dynamic point cloud compression using 3DTK toolkit and H.265\/HEVC. Proceedings of the 2019 2nd International Colloquium on Smart Grid Metrology (SMAGRIMET), Split, Croatia.","DOI":"10.23919\/SMAGRIMET.2019.8720392"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"da Silva Cruz, L.A., Dumi\u0107, E., Alexiou, E., Prazeres, J., Duarte, R., Pereira, M., Pinheiro, A., and Ebrahimi, T. (2019, January 5\u20137). Point cloud quality evaluation: Towards a definition for test conditions. Proceedings of the 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany.","DOI":"10.1109\/QoMEX.2019.8743258"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2012.10.004","article-title":"One billion points in the cloud\u2014An octree for efficient processing of 3D laser scans","volume":"76","author":"Elseberg","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"2","article-title":"Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration","volume":"3","author":"Elseberg","year":"2013","journal-title":"J. Softw. Eng. Robot."},{"key":"ref_13","unstructured":"(2021, September 13). 3DTK\u2014The 3D Toolkit. Available online: http:\/\/slam6d.sourceforge.net\/."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Houshiar, H., Borrmann, D., Elseberg, J., and N\u00fcchter, A. (2013, January 25\u201329). Panorama based point cloud reduction and registration. Proceedings of the 2013 16th International Conference on Advanced Robotics (ICAR), Montevideo, Uruguay.","DOI":"10.1109\/ICAR.2013.6766587"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Houshiar, H., and N\u00fcchter, A. (2015, January 29\u201331). 3D point cloud compression using conventional image compression for efficient data transmission. Proceedings of the 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/ICAT.2015.7340499"},{"key":"ref_16","unstructured":"Mammou, K., Chou, P.A., Flynn, D., Krivoku\u0107a, M., Nakagami, O., and Sugio, T. (2019). G-PCC Codec Description v2, MPEG. Technical Report, ISO\/IEC JTC1\/SC29\/WG11 Input Document N18189."},{"key":"ref_17","unstructured":"Zakharchenko, V. (2019). V-PCC Codec Description, MPEG. Technical Report, ISO\/IEC JTC1\/SC29\/WG11 Input Document N18190."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","article-title":"Overview of the High Efficiency Video Coding (HEVC) Standard","volume":"22","author":"Sullivan","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/JETCAS.2018.2885981","article-title":"Emerging MPEG Standards for Point Cloud Compression","volume":"9","author":"Schwarz","year":"2019","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e13","DOI":"10.1017\/ATSIP.2020.12","article-title":"An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC)","volume":"9","author":"Graziosi","year":"2020","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1017\/ATSIP.2019.20","article-title":"A comprehensive study of the rate-distortion performance in MPEG point cloud compression","volume":"8","author":"Alexiou","year":"2019","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Perry, S., Cong, H.P., da Silva Cruz, L.A., Prazeres, J., Pereira, M., Pinheiro, A., Dumic, E., Alexiou, E., and Ebrahimi, T. (2020, January 25\u201328). Quality Evaluation of Static Point Clouds Encoded Using MPEG Codecs. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191308"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alexiou, E., Tung, K., and Ebrahimi, T. (2020). Towards neural network approaches for point cloud compression. Proceedings Volume 11510, Applications of Digital Image Processing XLIII, International Society for Optics and Photonics.","DOI":"10.1117\/12.2569115"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TCSVT.2016.2543039","article-title":"Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video","volume":"27","author":"Mekuria","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Quach, M., Valenzise, G., and Dufaux, F. (2019, January 22\u201325). Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression. Proceedings of the 2019 IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803413"},{"key":"ref_26","unstructured":"Loop, C., Cai, Q., Escolano, S.O., and Chou, P. (2021, September 13). Microsoft Voxelized Upper Bodies\u2014A Voxelized Point Cloud Dataset; Technical Report, ISO\/IEC JTC1\/SC29 Joint WG11\/WG1 (MPEG\/JPEG) Input Document m38673\/M72012. Available online: http:\/\/plenodb.jpeg.org\/pc\/microsoft\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Quach, M., Valenzise, G., and Dufaux, F. (2020, January 21\u201324). Improved Deep Point Cloud Geometry Compression. Proceedings of the 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland.","DOI":"10.1109\/MMSP48831.2020.9287077"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/TCSVT.2021.3051377","article-title":"Lossy Point Cloud Geometry Compression via End-to-End Learning","volume":"31","author":"Wang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, D., Li, Z., and Ma, Z. (2021, January 23\u201326). Multiscale Point Cloud Geometry Compression. Proceedings of the 2021 Data Compression Conference (DCC), Virtual.","DOI":"10.1109\/DCC50243.2021.00015"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guarda, A.F.R., Rodrigues, N.M.M., and Pereira, F. (2019, January 12\u201315). Point Cloud Coding: Adopting a Deep Learning-based Approach. Proceedings of the 2019 Picture Coding Symposium (PCS), Ningbo, China.","DOI":"10.1109\/PCS48520.2019.8954537"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guarda, A.F.R., Rodrigues, N.M.M., and Pereira, F. (2020, January 21\u201324). Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability. Proceedings of the 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland.","DOI":"10.1109\/MMSP48831.2020.9287060"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/JSTSP.2020.3047520","article-title":"Adaptive Deep Learning-Based Point Cloud Geometry Coding","volume":"15","author":"Guarda","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Milani, S. (2021, January 19\u201322). ADAE: Adversarial Distributed Source Autoencoder For Point Cloud Compression. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506750"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lazzarotto, D., Alexiou, E., and Ebrahimi, T. (2021, January 19\u201322). On Block Prediction For Learning-Based Point Cloud Compression. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506429"},{"key":"ref_36","unstructured":"Yan, W., Shao, Y., Liu, S., Li, T.H., Li, Z., and Li, G. (2019). Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds. arXiv, Available online: https:\/\/arxiv.org\/abs\/1905.03691."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, T., and Liu, Y. (2019, January 21\u201325). 3D Point Cloud Geometry Compression on Deep Learning. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3351061"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Quach, M., Valenzise, G., and Duhamel, P. (2021, January 6\u201311). Learning-Based Lossless Compression of 3D Point Cloud Geometry. Proceedings of the ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414763"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Quach, M., Valenzise, G., and Duhamel, P. (2021, January 5\u20139). Multiscale deep context modeling for lossless point cloud geometry compression. Proceedings of the 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shenzhen, China.","DOI":"10.1109\/ICMEW53276.2021.9455990"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Que, Z., Lu, G., and Xu, D. (2021, January 19\u201325). VoxelContext-Net: An Octree based Framework for Point Cloud Compression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual.","DOI":"10.1109\/CVPR46437.2021.00598"},{"key":"ref_41","first-page":"223","article-title":"Learning residual coding for point clouds","volume":"Volume 11842","author":"Tescher","year":"2021","journal-title":"Applications of Digital Image Processing XLIV"},{"key":"ref_42","unstructured":"Weisstein, E.W. (2021, April 16). Map Projection. From MathWorld\u2014A Wolfram Web Resource. Available online: https:\/\/mathworld.wolfram.com\/topics\/MapProjections.html."},{"key":"ref_43","unstructured":"Houshiar, H. (2017). Documentation and Mapping with 3D Point Cloud Processing. [Ph.D. Thesis, University of W\u00fcrzburg]."},{"key":"ref_44","unstructured":"d\u2019Eon, E., Harrison, B., Myers, T., and Chou, P.A. (2021, September 13). 8i Voxelized Full Bodies\u2014A Voxelized Point Cloud Dataset; Technical Report, ISO\/IEC JTC1\/SC29 Joint WG11\/WG1 (MPEG\/JPEG) Input Document WG11M40059\/WG1M74006. Available online: https:\/\/jpeg.org\/plenodb\/pc\/8ilabs\/."},{"key":"ref_45","unstructured":"(2021, September 13). Lab, Visual Computing, MeshLab. Available online: http:\/\/www.meshlab.net\/."},{"key":"ref_46","unstructured":"(2019, February 06). CloudCompare\u20143D Point Cloud and Mesh Processing Software\u2014Open Source Project. Available online: http:\/\/www.cloudcompare.org."},{"key":"ref_47","unstructured":"FFmpeg Team (2021, May 02). FFmpeg. Available online: https:\/\/www.ffmpeg.org\/download.html."},{"key":"ref_48","unstructured":"Weisstein, E.W. (2021, November 06). Voronoi Diagram. From MathWorld\u2014A Wolfram Web Resource. Available online: https:\/\/mathworld.wolfram.com\/VoronoiDiagram.html."},{"key":"ref_49","unstructured":"Dumic, E. (2021, November 08). Scripts for Dynamic Point Cloud Compression. Available online: http:\/\/msl.unin.hr\/."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dumic, E., and da Silva Cruz, L.A. (2020). Point Cloud Coding Solutions, Subjective Assessment and Objective Measures: A Case Study. Symmetry, 12.","DOI":"10.3390\/sym12121955"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tian, D., Ochimizu, H., Feng, C., Cohen, R., and Vetro, A. (2017, January 17\u201320). Geometric distortion metrics for point cloud compression. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296925"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1051\/0004-6361\/201834841","article-title":"Quincuncial adaptive closed Kohonen (QuACK) map for the irregularly shaped comet 67P\/Churyumov-Gerasimenko","volume":"630","author":"Grieger","year":"2019","journal-title":"Astron. Astrophys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:54:56Z","timestamp":1760169296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,28]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010197"],"URL":"https:\/\/doi.org\/10.3390\/s22010197","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,12,28]]}}}