{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:51:40Z","timestamp":1772905900341,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry for Research and Education (MUR)","award":["ECS 00000038"],"award-info":[{"award-number":["ECS 00000038"]}]},{"name":"Italian Ministry for Research and Education (MUR)","award":["1409"],"award-info":[{"award-number":["1409"]}]},{"name":"Italian Ministry for Research and Education (MUR)","award":["CUP J53D23015030001"],"award-info":[{"award-number":["CUP J53D23015030001"]}]},{"name":"National Recovery and Resilience Plan (NRRP)","award":["ECS 00000038"],"award-info":[{"award-number":["ECS 00000038"]}]},{"name":"National Recovery and Resilience Plan (NRRP)","award":["1409"],"award-info":[{"award-number":["1409"]}]},{"name":"National Recovery and Resilience Plan (NRRP)","award":["CUP J53D23015030001"],"award-info":[{"award-number":["CUP J53D23015030001"]}]},{"name":"European Union","award":["ECS 00000038"],"award-info":[{"award-number":["ECS 00000038"]}]},{"name":"European Union","award":["1409"],"award-info":[{"award-number":["1409"]}]},{"name":"European Union","award":["CUP J53D23015030001"],"award-info":[{"award-number":["CUP J53D23015030001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>3D modeling and reconstruction are critical to creating immersive XR experiences, providing realistic virtual environments, objects, and interactions that increase user engagement and enable new forms of content manipulation. Today, 3D data can be easily captured using off-the-shelf, specialized headsets; very often, these tools provide real-time, albeit low-resolution, integration of continuously captured depth maps. This approach is generally suitable for basic AR and MR applications, where users can easily direct their attention to points of interest and benefit from a fully user-centric perspective. However, it proves to be less effective in more complex scenarios such as multi-user telepresence or telerobotics, where real-time transmission of local surroundings to remote users is essential. Two primary questions emerge: (i) what strategies are available for achieving real-time 3D reconstruction in such systems? and (ii) how can the effectiveness of real-time 3D reconstruction methods be assessed? This paper explores various approaches to the challenge of live 3D reconstruction from typical point cloud data. It first introduces some common data flow patterns that characterize virtual reality applications and shows that achieving high-speed data transmission and efficient data compression is critical to maintaining visual continuity and ensuring a satisfactory user experience. The paper thus introduces the concept of saliency-driven compression\/reconstruction and compares it with alternative state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/mti8050036","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T05:52:02Z","timestamp":1714715522000},"page":"36","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Saliency-Guided Point Cloud Compression for 3D Live Reconstruction"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4420-3363","authenticated-orcid":false,"given":"Pietro","family":"Ruiu","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4249-2834","authenticated-orcid":false,"given":"Lorenzo","family":"Mascia","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1420-910X","authenticated-orcid":false,"given":"Enrico","family":"Grosso","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Orts-Escolano, S., Rhemann, C., Fanello, S., Chang, W., Kowdle, A., Degtyarev, Y., Kim, D., Davidson, P.L., Khamis, S., and Dou, M. (2016, January 16\u201319). Holoportation: Virtual 3d teleportation in real-time. Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan.","DOI":"10.1145\/2984511.2984517"},{"key":"ref_2","unstructured":"Fernandez, S., Montagud, M., Rinc\u00f3n, D., Moragues, J., and Cernigliaro, G. (November, January 29). Addressing Scalability for Real-time Multiuser Holo-portation: Introducing and Assessing a Multipoint Control Unit (MCU) for Volumetric Video. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (December, January 30). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Kolkata, India.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17305","DOI":"10.1109\/ACCESS.2023.3245833","article-title":"Multimodal Multi-User Mixed Reality Human\u2013Robot Interface for Remote Operations in Hazardous Environments","volume":"11","author":"Szczurek","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TCSVT.2016.2580425","article-title":"A mixed reality telepresence system for collaborative space operation","volume":"27","author":"Fairchild","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.bjps.2023.10.130","article-title":"Ghana 3D Telemedicine International MDT: A proof-of-concept study","volume":"88","author":"Lo","year":"2024","journal-title":"J. Plast. Reconstr. Aesthetic Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.bjps.2022.10.012","article-title":"Participatory development of a 3D telemedicine system during COVID: The future of remote consultations","volume":"87","author":"Lo","year":"2023","journal-title":"J. Plast. Reconstr. Aesthetic Surg."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Murroni, M., Anedda, M., Fadda, M., Ruiu, P., Popescu, V., Zaharia, C., and Giusto, D. (2023). 6G\u2014Enabling the New Smart City: A Survey. Sensors, 23.","DOI":"10.3390\/s23177528"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hauswiesner, S., Straka, M., and Reitmayr, G. (2011, January 18\u201320). Coherent image-based rendering of real-world objects. Proceedings of the Symposium on Interactive 3D Graphics and Games, San Francisco, CA, USA.","DOI":"10.1145\/1944745.1944776"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alexiadis, D.S., Zarpalas, D., and Daras, P. (2013, January 10\u201312). Real-time, realistic full-body 3D reconstruction and texture mapping from multiple Kinects. Proceedings of the IVMSP 2013, Seoul, Republic of Korea.","DOI":"10.1109\/IVMSPW.2013.6611939"},{"key":"ref_11","first-page":"1","article-title":"Real-time 3D reconstruction at scale using voxel hashing","volume":"32","author":"Izadi","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/TVCG.2019.2899231","article-title":"SLAMCast: Large-scale, real-time 3D reconstruction and streaming for immersive multi-client live telepresence","volume":"25","author":"Stotko","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ishigaki, S.A.K., and Ismail, A.W. (2022, January 20\u201321). Real-time 3D reconstruction for mixed reality telepresence using multiple depth sensors. Proceedings of the International Conference on Advanced Communication and Intelligent Systems, Virtual.","DOI":"10.1007\/978-3-031-25088-0_5"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fadzli, F.E., Ismail, A.W., and Abd Karim Ishigaki, S. (2023). A systematic literature review: Real-time 3D reconstruction method for telepresence system. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0287155"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cao, C., Preda, M., and Zaharia, T. (2019, January 26\u201328). 3D point cloud compression: A survey. Proceedings of the 24th International Conference on 3D Web Technology, Los Angeles, CA, USA.","DOI":"10.1145\/3329714.3338130"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Nardo, F., Peressoni, D., Testolina, P., Giordani, M., and Zanella, A. (2022, January 10\u201313). Point cloud compression for efficient data broadcasting: A performance comparison. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA.","DOI":"10.1109\/WCNC51071.2022.9771764"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"art00005","DOI":"10.2352\/ISSN.2470-1173.2016.21.3DIPM-397","article-title":"Point Cloud Compression using Depth Maps","volume":"28","author":"Bletterer","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"846972","DOI":"10.3389\/frsip.2022.846972","article-title":"Survey on deep learning-based point cloud compression","volume":"2","author":"Quach","year":"2022","journal-title":"Front. Signal Process."},{"key":"ref_20","unstructured":"Pece, F., Kautz, J., and Weyrich, T. (2011, January 20\u201321). Adapting standard video codecs for depth streaming. Proceedings of the EGVE\/EuroVR, Nottingham, UK."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"J\u00e4ger, F. (2011, January 6\u20139). Contour-based segmentation and coding for depth map compression. Proceedings of the 2011 Visual Communications and Image Processing (VCIP), Tainan, Taiwan.","DOI":"10.1109\/VCIP.2011.6115989"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6529","DOI":"10.1007\/s11042-018-6327-4","article-title":"Depth compression via planar segmentation","volume":"78","author":"Kumar","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13761","DOI":"10.1007\/s11042-016-3727-1","article-title":"Depth map compression via 3D region-based representation","volume":"76","author":"Duch","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wilson, A.D. (2017, January 17\u201320). Fast lossless depth image compression. Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, Brighton, UK.","DOI":"10.1145\/3132272.3134144"},{"key":"ref_25","unstructured":"Sonoda, T., and Grunnet-Jepsen, A. (2024, April 26). Depth Image Compression by Colorization for Intel RealSense Depth Cameras. Intel Rev. 1.0. Available online: https:\/\/dev.intelrealsense.com\/docs\/depth-image-compression-by-colorization-for-intel-realsense-depth-cameras."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, M., Zhang, P., Chen, Z., Zhang, Y., Wang, X., and Kwong, S. (2022, January 16\u201319). End-to-end depth map compression framework via rgb-to-depth structure priors learning. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9898073"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13640-023-00606-z","article-title":"FitDepth: Fast and lite 16-bit depth image compression algorithm","volume":"2023","year":"2023","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zanuttigh, P., and Cortelazzo, G.M. (2009, January 4\u20136). Compression of depth information for 3D rendering. Proceedings of the 2009 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video, Potsdam, Germany.","DOI":"10.1109\/3DTV.2009.5069669"},{"key":"ref_29","unstructured":"Krishnamurthy, R., Chai, B.B., Tao, H., and Sethuraman, S. (2001, January 7\u201310). Compression and transmission of depth maps for image-based rendering. Proceedings of the 2001 International Conference on Image Processing (Cat. No. 01CH37205), Thessaloniki, Greece."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s11831-021-09602-w","article-title":"Computational 2D and 3D medical image data compression models","volume":"29","author":"Boopathiraja","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","first-page":"219","article-title":"Shifts in selective visual attention: Towards the underlying neural circuitry","volume":"4","author":"Koch","year":"1985","journal-title":"Hum. Neurobiol."},{"key":"ref_33","first-page":"353","article-title":"Learning to detect a salient object","volume":"33","author":"Liu","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TIP.2017.2787612","article-title":"Deep visual attention prediction","volume":"27","author":"Wang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11905","DOI":"10.1007\/s00521-021-05863-5","article-title":"On the correlation between human fixations, handcrafted and CNN features","volume":"33","author":"Cadoni","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s00422-023-00978-5","article-title":"Face detection based on a human attention guided multi-scale model","volume":"117","author":"Cadoni","year":"2023","journal-title":"Biol. Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0734-189X(85)90095-7","article-title":"\u201cForm-invariant\u201d topological mapping strategy for 2D shape recognition","volume":"30","author":"Massone","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_39","first-page":"266","article-title":"Video compression via log polar mapping","volume":"Volume 1295","author":"Weiman","year":"1990","journal-title":"Proceedings of the Real-Time Image Processing II"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.robot.2009.10.002","article-title":"A review of log-polar imaging for visual perception in robotics","volume":"58","author":"Traver","year":"2010","journal-title":"Robot. Auton. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1279920.1279925","article-title":"Distinctiveness of faces: A computational approach","volume":"5","author":"Bicego","year":"2008","journal-title":"ACM Trans. Appl. Percept."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.neucom.2004.10.065","article-title":"Features that draw visual attention: An information theoretic perspective","volume":"65\u201366","author":"Bruce","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Alexiou, E., and Ebrahimi, T. (2020, January 6\u201310). Towards a point cloud structural similarity metric. Proceedings of the 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), London, UK.","DOI":"10.1109\/ICMEW46912.2020.9106005"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Zhu, Y., Min, X., Wu, W., Chen, Y., and Zhai, G. (IEEE Trans. Multimed., 2023). Evaluating point cloud from moving camera videos: A no-reference metric, IEEE Trans. Multimed., early access.","DOI":"10.1109\/TMM.2023.3340894"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, W., Min, X., Zhou, Q., He, J., Wang, Q., and Zhai, G. (2022). MM-PCQA: Multi-modal learning for no-reference point cloud quality assessment. arXiv.","DOI":"10.24963\/ijcai.2023\/195"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1109\/TMM.2020.3033117","article-title":"Predicting the perceptual quality of point cloud: A 3d-to-2d projection-based exploration","volume":"23","author":"Yang","year":"2020","journal-title":"IEEE Trans. Multimed."}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/8\/5\/36\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:39:11Z","timestamp":1760107151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/8\/5\/36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,3]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["mti8050036"],"URL":"https:\/\/doi.org\/10.3390\/mti8050036","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,3]]}}}