{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:24:02Z","timestamp":1761402242180,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62001262","62001263","ZR2020QF008"],"award-info":[{"award-number":["62001262","62001263","ZR2020QF008"]}]},{"name":"Nature Science Foundation of Shandong Province","award":["62001262","62001263","ZR2020QF008"],"award-info":[{"award-number":["62001262","62001263","ZR2020QF008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra\/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1\/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.<\/jats:p>","DOI":"10.3390\/s22145108","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T22:11:47Z","timestamp":1657231907000},"page":"5108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Qiang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"},{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liuyang","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"},{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4867-2282","authenticated-orcid":false,"given":"Xuebin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-6483","authenticated-orcid":false,"given":"Zhaopeng","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shizhong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Wang, C., Ji, M., Wang, J., Wen, W., Li, T., and Sun, Y. (2019). An improved DBSCAN method for LiDAR data segmentation with automatic Eps estimation. Sensors, 19.","key":"ref_1","DOI":"10.3390\/s19010172"},{"doi-asserted-by":"crossref","unstructured":"McGlade, J., Wallace, L., Reinke, K., and Jones, S. (2022). The potential of low-cost 3D imaging technologies for forestry applications: Setting a research agenda for low-cost remote sensing inventory tasks. Forests, 13.","key":"ref_2","DOI":"10.3390\/f13020204"},{"doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry remote sensing from unmanned aerial vehicles: A review focusing on the data, processing and potentialities. Remote Sens., 12.","key":"ref_3","DOI":"10.3390\/rs12061046"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110467","DOI":"10.1016\/j.oceaneng.2021.110467","article-title":"Wave height predictions in complex sea flows through soft-computing models: Case study of Persian Gulf","volume":"245","author":"Sadeghifar","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112282","DOI":"10.1016\/j.rse.2020.112282","article-title":"Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behaviour","volume":"255","author":"Gale","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3390\/w7020420","article-title":"Determining characteristic vegetation areas by terrestrial laser scanning for floodplain flow modeling","volume":"7","author":"Jalonen","year":"2015","journal-title":"Water"},{"doi-asserted-by":"crossref","unstructured":"Lama, G.F.C., Sadeghifar, T., Azad, M.T., Sihag, P., and Kisi, O. (2022). On the indirect estimation of wind wave heights over the southern coasts of Caspian Sea: A comparative analysis. Water, 14.","key":"ref_7","DOI":"10.3390\/w14060843"},{"doi-asserted-by":"crossref","unstructured":"Godone, D., Allasia, P., Borrelli, L., and Gull\u00e0, G. (2020). UAV and structure from motion approach to monitor the maierato landslide evolution. Remote Sens., 12.","key":"ref_8","DOI":"10.3390\/rs12061039"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106261","DOI":"10.1016\/j.enggeo.2021.106261","article-title":"Hyperspectral remote sensing for detecting geotechnical problems at Ray mine","volume":"292","author":"He","year":"2021","journal-title":"Eng. Geol."},{"doi-asserted-by":"crossref","unstructured":"Rao, Y., Zhang, M., Cheng, Z., Xue, J., Pu, J., and Wang, Z. (2021). Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF. Sensors, 21.","key":"ref_10","DOI":"10.3390\/s21082731"},{"doi-asserted-by":"crossref","unstructured":"Xue, G., Wei, J., Li, R., and Cheng, J. (2022). LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine. Sensors, 22.","key":"ref_11","DOI":"10.3390\/s22020520"},{"doi-asserted-by":"crossref","unstructured":"Xiong, L., Fu, Z., Zeng, D., and Leng, B. (2021). An optimized trajectory planner and motion controller framework for autonomous driving in unstructured environments. Sensors, 21.","key":"ref_12","DOI":"10.3390\/s21134409"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107650","DOI":"10.1016\/j.ecss.2021.107650","article-title":"Validation of Landsat 8 high resolution Sea Surface Temperature using surfers","volume":"265","author":"Vanhellemont","year":"2021","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2016.10.011","article-title":"A review of differential absorption algorithms utilized at NOAA for measuring sea surface temperature with satellite radiometers","volume":"187","author":"Walton","year":"2016","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Lama, G.F.C., Rillo Migliorini Giovannini, M., Errico, A., Mirzaei, S., Padulano, R., Chirico, G.B., and Preti, F. (2021). Hydraulic efficiency of green-blue flood control scenarios for vegetated rivers: 1D and 2D unsteady simulations. Water, 13.","key":"ref_15","DOI":"10.3390\/w13192620"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1177\/0278364912458814","article-title":"Challenging data sets for point cloud registration algorithms","volume":"31","author":"Pomerleau","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10514-016-9548-2","article-title":"Low-drift and real-time lidar odometry and mapping","volume":"41","author":"Zhang","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_18","first-page":"284","article-title":"Graph-based static 3D point clouds geometry coding","volume":"21","author":"Brites","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_19","first-page":"2217","article-title":"A volumetric approach to point cloud compression\u2014Part ii: Geometry compression","volume":"29","author":"Chou","year":"2019","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Guede, C., Andrivon, P., Marvie, J.E., Ricard, J., Redmann, B., and Chevet, J.C. (2020, January 10\u201312). V-PCC: Performance evaluation of the first MPEG Point Cloud Codec. Proceedings of the SMPTE 2020 Annual Technical Conference and Exhibition, Virtual.","key":"ref_20","DOI":"10.5594\/M001913"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2012.10.004","article-title":"One billion points in the cloud\u2013an octree for efficient processing of 3D laser scans","volume":"76","author":"Elseberg","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TITS.2019.2956066","article-title":"Motion analysis and performance improved method for 3D LiDAR sensor data compression","volume":"22","author":"Tu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/TCSVT.2015.2416571","article-title":"Fast depth video compression for mobile RGB-D sensors","volume":"26","author":"Wang","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"unstructured":"Tu, C., Takeuchi, E., Miyajima, C., and Takeda, K. (2016, January 1\u20134). Compressing continuous point cloud data using image compression methods. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Janeiro, Brazil.","key":"ref_24"},{"doi-asserted-by":"crossref","unstructured":"Feng, Y., Liu, S., and Zhu, Y. (2020, January 25\u201329). Real-time spatio-temporal lidar point cloud compression. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","key":"ref_25","DOI":"10.1109\/IROS45743.2020.9341071"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113616","DOI":"10.1109\/ACCESS.2019.2935253","article-title":"Real-time streaming point cloud compression for 3d lidar sensor using u-net","volume":"7","author":"Tu","year":"2019","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Tu, C., Takeuchi, E., Carballo, A., and Takeda, K. (2019, January 20\u201324). Point cloud compression for 3D LiDAR sensor using recurrent neural network with residual blocks. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","key":"ref_27","DOI":"10.1109\/ICRA.2019.8794264"},{"unstructured":"Google (2022, May 06). Draco: 3D Data Compression. Available online: https:\/\/github.com\/google\/draco.","key":"ref_28"},{"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), Washington, DC, USA.","key":"ref_29","DOI":"10.1109\/ICAT.2015.7340499"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Yeh, R.A., Tang, X., Liu, Y., and Agarwala, A. (2017, January 22\u201329). Video frame synthesis using deep voxel flow. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","key":"ref_30","DOI":"10.1109\/ICCV.2017.478"},{"doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., and Stachniss, C. (2019, January 3\u20138). Rangenet++: Fast and accurate lidar semantic segmentation. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","key":"ref_31","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1109\/TITS.2020.3034879","article-title":"A novel coding architecture for multi-line LiDAR point clouds based on clustering and convolutional LSTM network","volume":"23","author":"Sun","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Langer, F., Milioto, A., Haag, A., Behley, J., and Stachniss, C. (2020, January 25\u201329). Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","key":"ref_33","DOI":"10.1109\/IROS45743.2020.9341508"},{"doi-asserted-by":"crossref","unstructured":"Sun, X., Wang, S., Wang, M., Cheng, S.S., and Liu, M. (2020, January 12\u201316). An advanced LiDAR point cloud sequence coding scheme for autonomous driving. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","key":"ref_34","DOI":"10.1145\/3394171.3413537"},{"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 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","key":"ref_35","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.isprsjprs.2021.11.022","article-title":"A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment-ScienceDirect","volume":"183","author":"Yang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s11263-021-01551-y","article-title":"Learning Scene Dynamics from Point Cloud Sequences","volume":"130","author":"He","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10846-021-01545-5","article-title":"Place recognition and navigation of outdoor mobile robots based on random Forest learning with a 3D LiDAR","volume":"104","author":"Zhou","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s00530-021-00856-9","article-title":"Point cloud inpainting with normal-based feature matching","volume":"28","author":"Shi","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9326","DOI":"10.1007\/s11227-021-04297-z","article-title":"Secure and effective assured deletion scheme with orderly overwriting for cloud data","volume":"78","author":"Tian","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_42","first-page":"4357","article-title":"Gabor Convolutional Networks","volume":"2018","author":"Luan","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1007\/s11263-016-0880-y","article-title":"Bounding multiple gaussians uncertainty with application to object tracking","volume":"118","author":"Zhang","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1109\/TIP.2017.2718189","article-title":"Action recognition using 3D histograms of texture and a multi-class boosting classifier","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:44:03Z","timestamp":1760139843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,7]]},"references-count":44,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145108"],"URL":"https:\/\/doi.org\/10.3390\/s22145108","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,7]]}}}