{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:05:43Z","timestamp":1782313543309,"version":"3.54.5"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2023,5,17]]},"abstract":"<jats:p>The prevalent point cloud compression (PCC) standards of today are utilized to encode various types of point cloud data, allowing for reasonable bandwidth and storage usage. With increasing demand for high-fidelity three-dimensional (3D) models for a large variety of applications, including immersive visual communication, Augmented reality (AR) and Virtual Reality (VR), navigation, autonomous driving, and smart city, point clouds are seeing increasing usage and development to meet the increasing demands. However, with the advancements in 3D modelling and sensing, the amount of data required to accurately depict such representations and models is likewise ballooning to increasingly large proportions, leading to the development and standardization of the point cloud compression standards. In this article, we provide an overview of some topical and popular MPEG point cloud compression (PCC) standards. We discuss the development and applications of the Geometry-based PCC (G-PCC) and Video-based PCC (V-PCC) standards as they escalate in importance in an era of virtual reality and machine learning. Finally, we conclude our article describing the future research directions and applications of the PCC standards of today.<\/jats:p>","DOI":"10.1145\/3599184.3599188","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T23:52:21Z","timestamp":1684799541000},"page":"11-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["An Introduction to Point Cloud Compression Standards"],"prefix":"10.1145","volume":"27","author":[{"given":"Anthony","family":"Chen","sequence":"first","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiwen","family":"Mao","sequence":"additional","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Li","sequence":"additional","affiliation":[{"name":"University of Missouri, Kansas City, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minrui","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics at Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Han","sequence":"additional","affiliation":[{"name":"University of Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"N 19526","author":"Codec Description V-PCC","year":"2020","unstructured":"\" V-PCC Codec Description ,\" document ISO\/ IEC JTC 1\/SC 29\/WG 11 MPEG , N 19526 , 3D Graphics , Sept. 2020 . \"V-PCC Codec Description,\" document ISO\/IEC JTC 1\/SC 29\/WG 11 MPEG, N 19526, 3D Graphics, Sept. 2020."},{"key":"e_1_2_1_2_1","volume-title":"Apr.","author":"Codec Description G-PCC","year":"2020","unstructured":"\" G-PCC Codec Description ,\" document ISO\/ IEC JTC 1\/SC 29\/WG 7 MPEG, N00271 , 3D Graphics , Apr. 2020 . \"G-PCC Codec Description,\" document ISO\/IEC JTC 1\/SC 29\/WG 7 MPEG, N00271, 3D Graphics, Apr. 2020."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"D. Graziosi O. Nakagami S. Kuma A. Zaghetto T. Suzuki and A. Tabatabai. April 2020. An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC) APSIPA Transactions on Signal and Information Processing vol.9 e13 1--17.  D. Graziosi O. Nakagami S. Kuma A. Zaghetto T. Suzuki and A. Tabatabai. April 2020. An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC) APSIPA Transactions on Signal and Information Processing vol.9 e13 1--17.","DOI":"10.1017\/ATSIP.2020.12"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2018.2885981"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2900721"},{"key":"e_1_2_1_6_1","volume-title":"July","author":"Available Performance Analysis","year":"2022","unstructured":"Performance Analysis of Currently AI-based Available Solutions for PCC,\" document ISO\/ IEC JTC 1\/SC 29\/WG 7 , N 00374m 3D Graphics , July 2022 . Performance Analysis of Currently AI-based Available Solutions for PCC,\" document ISO\/IEC JTC 1\/SC 29\/WG 7, N 00374m 3D Graphics, July 2022."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3325867.3325873"},{"key":"e_1_2_1_8_1","volume-title":"21. Adaptive deep learning-based point cloud geometry coding","author":"Guarda A.F.R.","unstructured":"A.F.R. Guarda , N.M.M. Rodrigues , and F. Pereira . Feb . 21. Adaptive deep learning-based point cloud geometry coding . IEEE Journal on Selected Topics in Signal Processing , vol. 15 , no.2, 415--430. A.F.R. Guarda, N.M.M. Rodrigues, and F. Pereira. Feb. 21. Adaptive deep learning-based point cloud geometry coding. IEEE Journal on Selected Topics in Signal Processing, vol.15, no.2, 415--430."},{"key":"e_1_2_1_9_1","volume-title":"Apr.","author":"Point Cloud Call","year":"2017","unstructured":"\" Call for Proposals for Point Cloud Compression v2,\" document ISO\/ IEC JTC 1\/SC 29\/WG 11 MPEG, N16763 , 3D Graphics , Apr. 2017 . \"Call for Proposals for Point Cloud Compression v2,\" document ISO\/IEC JTC 1\/SC 29\/WG 11 MPEG, N16763, 3D Graphics, Apr. 2017."},{"key":"e_1_2_1_10_1","volume-title":"Proc. SoutheastCon","author":"Hooda R.","year":"2022","unstructured":"R. Hooda , W. D. Pan , and T. M. Syed . Mar.\/Apr.2022. A survey on 3D point cloud compression using machine learning approaches . Proc. SoutheastCon 2022 , Mobile, AL, 522--529. R. Hooda, W. D. Pan, and T. M. Syed. Mar.\/Apr.2022. A survey on 3D point cloud compression using machine learning approaches. Proc. SoutheastCon 2022, Mobile, AL, 522--529."},{"key":"e_1_2_1_11_1","volume-title":"Proc. 2019 IEEE\/ CVF Conference on Computer Vision and Pattern Recognition","author":"Choy C.","unstructured":"C. Choy , J. Gwak , and S. Savarese . June 2019. 4D spatio-temporal convnets: Minkowski convolutional neural networks . Proc. 2019 IEEE\/ CVF Conference on Computer Vision and Pattern Recognition , Long Beach, CA, 3075--3084. C. Choy, J. Gwak, and S. Savarese. June 2019. 4D spatio-temporal convnets: Minkowski convolutional neural networks. Proc. 2019 IEEE\/ CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 3075--3084."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3180904"},{"issue":"1","key":"e_1_2_1_13_1","first-page":"2866","article-title":"Video-based point cloud compression artifact removal","volume":"24","author":"Akhtar A.","year":"2021","unstructured":"A. Akhtar , W. Gao , L. Li , Z. Li , W. Jia , and S. Liu . Jan. 2021 . Video-based point cloud compression artifact removal . IEEE Transactions on Multimedia , vol. 24 , no. 1 , 2866 -- 2876 . A. Akhtar, W. Gao, L. Li, Z. Li, W. Jia, and S. Liu. Jan. 2021. Video-based point cloud compression artifact removal. IEEE Transactions on Multimedia, vol.24, no.1, 2866--2876.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_2_1_14_1","volume-title":"Sparse tensor-based multiscale representation for point cloud geometry compression","author":"Wang J.","unstructured":"J. Wang , D. Ding , Z. Li , X. Feng , C. Cao , and Z. Ma . Sparse tensor-based multiscale representation for point cloud geometry compression . IEEE Transactions on Pattern Analysis & Machine Intelligence , in press. DOI 10.1109\/ TPAMI.2022.3225816. J. Wang, D. Ding, Z. Li, X. Feng, C. Cao, and Z. Ma. Sparse tensor-based multiscale representation for point cloud geometry compression. IEEE Transactions on Pattern Analysis & Machine Intelligence, in press. DOI 10.1109\/ TPAMI.2022.3225816."},{"key":"e_1_2_1_15_1","volume-title":"Proc. Thirty- First International Joint Conference on Artificial Intelligence (IJCAI-22)","author":"Fan T.","unstructured":"T. Fan , L. Gao , Y. Xu , Z. Li , and D. Wang . July 2022. D-DPCC: Deep dynamic point cloud compression via 3D motion prediction . Proc. Thirty- First International Joint Conference on Artificial Intelligence (IJCAI-22) , Vienna, Austria, 898--904. T. Fan, L. Gao, Y. Xu, Z. Li, and D. Wang. July 2022. D-DPCC: Deep dynamic point cloud compression via 3D motion prediction. Proc. Thirty- First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria, 898--904."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3086711"},{"key":"e_1_2_1_17_1","volume-title":"Proc. IEEE Workshop on Multimedia Signal Processing (MMSP)","author":"Hou L.","unstructured":"L. Hou , L. Gao , Y. Xu , Z. Li , X. Xu , and S. Liu . Sept . 2022. Learning-based intra-prediction for point cloud attribute transform coding . Proc. IEEE Workshop on Multimedia Signal Processing (MMSP) , Shanghai, China. 1--6. L. Hou, L. Gao, Y. Xu, Z. Li, X. Xu, and S. Liu. Sept. 2022. Learning-based intra-prediction for point cloud attribute transform coding. Proc. IEEE Workshop on Multimedia Signal Processing (MMSP), Shanghai, China. 1--6."},{"issue":"5","key":"e_1_2_1_18_1","first-page":"2352","article-title":"Convolutional neural network-based occupancy map accuracy improvement for video-based point cloud compression","volume":"24","author":"Jia W.","year":"2021","unstructured":"W. Jia , L. Li , Z. Li , and S. Liu . May 2021 . Convolutional neural network-based occupancy map accuracy improvement for video-based point cloud compression , IEEE Transactions on Multimedia , vol. 24 , no. 5 , 2352 -- 2365 . W. Jia, L. Li, Z. Li, and S. Liu. May 2021. Convolutional neural network-based occupancy map accuracy improvement for video-based point cloud compression, IEEE Transactions on Multimedia, vol.24, no.5, 2352--2365.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01503-6"},{"key":"e_1_2_1_20_1","volume-title":"Proc. 2023 IEEE Data Compression Conference (DCC)","author":"Wang P.","unstructured":"P. Wang , S. Wang and Z. Li. Mar . 2023. Occupancy map guided attributes deblocking for video-based point cloud compression , Proc. 2023 IEEE Data Compression Conference (DCC) , Snowbird, UT. P. Wang, S. Wang and Z. Li. Mar. 2023. Occupancy map guided attributes deblocking for video-based point cloud compression, Proc. 2023 IEEE Data Compression Conference (DCC), Snowbird, UT."}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599184.3599188","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3599184.3599188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:04Z","timestamp":1750178284000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3599184.3599188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,17]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,5,17]]}},"alternative-id":["10.1145\/3599184.3599188"],"URL":"https:\/\/doi.org\/10.1145\/3599184.3599188","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"value":"2375-0529","type":"print"},{"value":"2375-0537","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,17]]},"assertion":[{"value":"2023-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}