{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:41Z","timestamp":1760145221711,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"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>A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points\u2019 proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate\u2013distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.<\/jats:p>","DOI":"10.3390\/s24134285","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improved Video-Based Point Cloud Compression via Segmentation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3136-2584","authenticated-orcid":false,"given":"Faranak","family":"Tohidi","sequence":"first","affiliation":[{"name":"School of Computing Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-5056","authenticated-orcid":false,"given":"Manoranjan","family":"Paul","sequence":"additional","affiliation":[{"name":"School of Computing Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5145-7276","authenticated-orcid":false,"given":"Anwaar","family":"Ulhaq","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Centre for Intelligent Systems, Central Queensland University, Sydney Campus, Rockhampton, QLD 4701, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-5424","authenticated-orcid":false,"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia"},{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Griffith Business School, Griffith University, Brisbane, QLD 4111, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/JPROC.2021.3085957","article-title":"Compression of sparse and dense dynamic point clouds\u2014Methods and standards","volume":"109","author":"Cao","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TBC.2019.2957652","article-title":"A comprehensive study and comparison of core technologies for MPEG 3-D point cloud compression","volume":"66","author":"Liu","year":"2019","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bhandari, V., Phillips, T.G., and McAree, P.R. (2023). Real-Time 6-DOF Pose Estimation of Known Geometries in Point Cloud Data. Sensors, 23.","DOI":"10.3390\/s23063085"},{"key":"ref_4","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_5","first-page":"555","article-title":"A voxelize structured refinement method for registration of point clouds from Kinect sensors","volume":"22","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wan, R., Zhao, T., and Zhao, W. (2023). PTA-Det: Point Transformer Associating Point Cloud and Image for 3D Object Detection. Sensors, 23.","DOI":"10.3390\/s23063229"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, M.Y., Lee, S.H., Jung, K.D., Lee, S.H., and Kwon, S.C. (2021). A novel preprocessing method for dynamic point-cloud compression. Appl. Sci., 11.","DOI":"10.3390\/app11135941"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4561","DOI":"10.1109\/TCSVT.2021.3101807","article-title":"Adaptive geometry partition for point cloud compression","volume":"31","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, G., Li, X., Sun, S., and Yi, W. (2023). Robust and Fast Normal Mollification via Consistent Neighborhood Reconstruction for Unorganized Point Clouds. Sensors, 23.","DOI":"10.3390\/s23063292"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s41598-023-50658-4","article-title":"Surface and underwater human pose recognition based on temporal 3D point cloud deep learning","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_11","unstructured":"Lee, L.H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., and Hui, P. (2021). All one needs to know about Metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4054","DOI":"10.1038\/s41598-022-08030-5","article-title":"Exploring the mechanical and morphological rationality of tree branch structure based on 3D point cloud analysis and the finite element method","volume":"12","author":"Tsugawa","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gao, W., and Liu, S. (2020, January 24\u201327). Implicit geometry partition for point cloud compression. Proceedings of the 2020 Data Compression Conference (DCC), Snowbird, UT, USA.","DOI":"10.1109\/DCC47342.2020.00015"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/LRA.2019.2900747","article-title":"A novel point cloud compression algorithm based on clustering","volume":"4","author":"Sun","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15298","DOI":"10.1109\/ACCESS.2022.3148252","article-title":"LiDAR point cloud compression by vertically placed objects based on global motion prediction","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1007\/s11042-021-11672-8","article-title":"Block size selection in rate-constrained geometry based point cloud compression","volume":"81","author":"Gao","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Akhtar, A., Li, Z., Van der Auwera, G., and Chen, J. (2022, January 23\u201327). Dynamic point cloud interpolation. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747105"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.1109\/TMM.2021.3090148","article-title":"Video-based point cloud compression artefact removal","volume":"24","author":"Akhtar","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tohidi, F., Manoranjan, P., and Ulhaq, A. (2022, January 12\u201314). Dynamic point cloud compression with cross-sectional approach. Proceedings of the Pacific-Rim Symposium on Image and Video Technology, Auckland, New Zealand.","DOI":"10.1007\/978-3-031-26431-3_6"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tohidi, F., Paul, M., and Ulhaq, A. (December, January 30). Dynamic Point Cloud Compression using Slicing focused on Self-occluded Points. Proceedings of the 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia.","DOI":"10.1109\/DICTA56598.2022.10034563"},{"key":"ref_21","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_22","unstructured":"Perry, S. (2024, June 18). Common Test Conditions for Point Cloud Compression. ISO\/IEC JTC1\/SC29\/WG11 Doc. N18474, Available online: https:\/\/ds.jpeg.org\/documents\/jpegpleno\/wg1n88044-CTQ-JPEG_Pleno_PCC_Common_Test_Conditions_3_3.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TBC.2024.3353568","article-title":"Occupancy-Assisted Attribute Artifact Reduction for Video-Based Point Cloud Compression","volume":"70","author":"Gao","year":"2024","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1109\/TIP.2011.2134858","article-title":"Optimal compression plane for efficient video coding","volume":"20","author":"Liu","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, L., Li, Z., Liu, S., and Li, H. (2019). Video-based compression for plenoptic point clouds. arXiv.","DOI":"10.1109\/DCC47342.2020.00053"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1109\/TMM.2020.3016894","article-title":"Efficient projected frame padding for video-based point cloud compression","volume":"23","author":"Li","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lu, J., Zhang, W., Yang, L., and Yang, F. (2022, January 16\u201319). Distribution-Driven Predictor Screening For Point Cloud Attribute Compression. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897688"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dumic, E., Bjelopera, A., and N\u00fcchter, A. (2021). Dynamic point cloud compression based on projections, surface reconstruction and video compression. Sensors, 22.","DOI":"10.3390\/s22010197"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"80088","DOI":"10.1109\/ACCESS.2021.3084180","article-title":"Fast grid-based refining segmentation method in video-based point cloud compression","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Subramanyam, S., Viola, I., Hanjalic, A., and Cesar, P. (2020, January 12\u201316). User centered adaptive streaming of dynamic point clouds with low complexity tiling. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413535"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Seidel, I., Freitas, D.R., Dorea, C., Garcia, D.C., Ferreira, R.U., Higa, R., de Queiroz, R.L., and Testoni, V. (2021, January 19\u201322). Memory-friendly segmentation refinement for video-based point cloud compression. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506515"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahmmed, A., Paul, M., Murshed, M., and Taubman, D. (2021, January 6\u201311). Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414171"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ahmmed, A., Paul, M., Murshed, M., and Taubman, D. (2021, January 19\u201322). Dynamic point cloud geometry compression using cuboid based commonality modeling framework. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506333"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/TIP.2022.3152065","article-title":"Learning-based rate control for video-based point cloud compression","volume":"31","author":"Wang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6237","DOI":"10.1109\/TIP.2020.2989576","article-title":"Rate control for video-based point cloud compression","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ahmmed, A., Paul, M., and Pickering, M. (2022, January 22\u201325). Dynamic point cloud texture video compression using the edge position difference oriented motion model. Proceedings of the 2021 Data Compression Conference (DCC), Snowbird, UT, USA.","DOI":"10.1109\/DCC50243.2021.00075"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/TCSVT.2021.3063501","article-title":"Occupancy map guided fast video-based dynamic point cloud coding","volume":"32","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83538","DOI":"10.1109\/ACCESS.2020.2991478","article-title":"3D motion estimation and compensation method for video-based point cloud compression","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TIP.2019.2931621","article-title":"Advanced 3D motion prediction for video-based dynamic point cloud compression","volume":"29","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Costa, A., Dricot, A., Brites, C., Ascenso, J., and Pereira, F. (2019, January 27\u201329). Improved patch packing for the MPEG V-PCC standard. Proceedings of the 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/MMSP.2019.8901690"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/TCSVT.2020.2985911","article-title":"View-dependent dynamic point cloud compression","volume":"31","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/TCSVT.2020.2966118","article-title":"Occupancy-map-based rate distortion optimization and partition for video-based point cloud compression","volume":"31","author":"Li","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"207805","DOI":"10.1109\/ACCESS.2020.3038800","article-title":"Contextual homogeneity-based patch decomposition method for higher point cloud compression","volume":"8","author":"Rhyu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tohidi, F., and Paul, M. (2023, January 8\u201311). Dynamic Point Cloud Compression Approach Using Hexahedron Segmentation. Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIP49359.2023.10222382"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhu, W., Xu, Y., Xu, Y., and Yang, L. (2021, January 22\u201328). Visual quality optimization for view-dependent point cloud compression. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401619"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Que, Z., Lu, G., and Xu, D. (2021, January 20\u201325). Voxelcontext-net: An octree based framework for point cloud compression. 2021 IEEE. Proceedings of the CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00598"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/TCSVT.2022.3197370","article-title":"Isolated points prediction via deep neural network on point cloud lossless geometry compression","volume":"33","author":"Wei","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/TMM.2021.3079698","article-title":"Convolutional neural network-based occupancy map accuracy improvement for video-based point cloud compression","volume":"24","author":"Jia","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107688","DOI":"10.1109\/ACCESS.2021.3102029","article-title":"Denoising and inpainting for point clouds compressed by V-PCC","volume":"9","author":"Cao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1007\/s11263-021-01503-6","article-title":"Deep learning geometry compression artefacts removal for video-based point cloud compression","volume":"129","author":"Jia","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Camuffo, E., Mari, D., and Milani, S. (2022). Recent advancements in learning algorithms for point clouds: An updated overview. Sensors, 22.","DOI":"10.3390\/s22041357"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lopes, E., Ascenso, J., Brites, C., and Pereira, F. (2019, January 8\u201312). Adaptive plane projection for video-based point cloud coding. Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China.","DOI":"10.1109\/ICME.2019.00017"},{"key":"ref_53","unstructured":"Sevom, V.F., Schwarz, S., and Gabbouj, M. (2018, January 26\u201328). Geometry-guided 3D data interpolation for projection-based dynamic point cloud coding. Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sheikhipour, N., Pesonen, M., Schwarz, S., and Vadakital, V.K.M. (2019, January 28\u201331). Improved single-layer coding of volumetric data. Proceedings of the 2019 8th European Workshop on Visual Information Processing (EUVIP), Rome, Italy.","DOI":"10.1109\/EUVIP47703.2019.8946131"},{"key":"ref_55","first-page":"11","article-title":"8i Voxelized Full Bodies-a Voxelized Point Cloud Dataset","volume":"Volume 7","author":"Harrison","year":"2017","journal-title":"ISO\/IEC JTC1\/SC29 Jt. WG11\/WG1 (MPEG\/JPEG) Input Document WG11M40059\/WG1M74006"},{"key":"ref_56","unstructured":"Xu, Y., Lu, Y., and Wen, Z. (2017, January 23\u201327). Owlii dynamic human mesh sequence dataset. Proceedings of the ISO\/IEC JTC1\/SC29\/WG11 m41658, 120th MPEG Meeting, Macau, China."},{"key":"ref_57","unstructured":"Loop, C., Cai, Q., Escolano, S.O., and Chou, P.A. (2024, June 18). JPEG Pleno Database: Microsoft Voxelized Upper Bodies-A Voxelized Point Cloud Dataset. ISO\/IEC JTC1\/SC29 Joint WG11\/WG1 (MPEG\/JPEG) input document m38673\/M72012, Available online: https:\/\/plenodb.jpeg.org\/pc\/microsoft."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4285\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:08:44Z","timestamp":1760108924000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4285"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,1]]},"references-count":57,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134285"],"URL":"https:\/\/doi.org\/10.3390\/s24134285","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,7,1]]}}}