{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:16:50Z","timestamp":1775470610609,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2014,8,4]],"date-time":"2014-08-04T00:00:00Z","timestamp":1407110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input\/Output (I\/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I\/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I\/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points.<\/jats:p>","DOI":"10.3390\/rs6087212","type":"journal-article","created":{"date-parts":[[2014,8,4]],"date-time":"2014-08-04T08:06:18Z","timestamp":1407139578000},"page":"7212-7232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures"],"prefix":"10.3390","volume":"6","author":[{"given":"Xiaochen","family":"Kang","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangguo","family":"Lin","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Riedl, A., Kainz, W., and Elmes, G. (2006). Progress in Spatial Data Handling, Springer.","DOI":"10.1007\/3-540-35589-8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/19479832.2013.811124","article-title":"3D building modeling using images and LiDAR: A review","volume":"4","author":"Wang","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hammoudi, K., Dornaika, F., Soheilian, B., and Paparoditis, N. (2010, January 1\u20133). Extracting wire-frame models of street facades from 3D point clouds and the corresponding cadastral map. Saint-Mand\u00e9, Paris, France.","DOI":"10.1109\/CRV.2010.23"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.geomorph.2008.09.015","article-title":"LIDAR monitoring of mass wasting processes: The Radicofani Landslide, Province of Siena, Central Italy","volume":"105","author":"Baldo","year":"2009","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1002\/esp.482","article-title":"Application of airborne LiDAR in river environments: The River Coquet, Northumberland, UK","volume":"28","author":"Charlton","year":"2003","journal-title":"Earth Surf. Process. Landf"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"307","DOI":"10.2112\/05-0548.1","article-title":"Application of airborne LIDAR for seacliff volumetric change and beach-sediment budget contributions","volume":"22","author":"Young","year":"2006","journal-title":"J. Coast. Res"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1166\/sl.2012.1826","article-title":"Object-based classification of airborne light detection and ranging point clouds in human settlements","volume":"10","author":"Shen","year":"2012","journal-title":"Sens. Lett"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.3390\/rs5083749","article-title":"SVM-based classification of segmented Airborne LiDAR point clouds in urban areas","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens"},{"key":"ref_9","first-page":"109","article-title":"Airborne LiDAR data processing and information extraction","volume":"73","author":"Chen","year":"2007","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_10","unstructured":"Isenburg, M., Liu, Y., Shewchuk, J., and Snoeyink, J. (August, January 30). Streaming computation of delaunay triangulations. Boston, MA, USA."},{"key":"ref_11","first-page":"3","article-title":"LiDAR activities and research priorities in the commercial sector","volume":"34","author":"Flood","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr"},{"key":"ref_13","first-page":"111","article-title":"DEM generation from laser scanner data using adaptive TIN models","volume":"33","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.04.001","article-title":"Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive tin densification","volume":"81","author":"Zhang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.3390\/rs6021294","article-title":"Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments","volume":"6","author":"Lin","year":"2014","journal-title":"Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LiDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2012.08.003","article-title":"Ground filtering and vegetation mapping using multi-return terrestrial laser scanning","volume":"76","author":"Pirotti","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_18","first-page":"935","article-title":"Slope based filtering of laser altimetry data","volume":"33","author":"Vosselman","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2004.05.004","article-title":"Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds","volume":"59","author":"Sithole","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-3-540-87744-8_2","article-title":"A bridging model for multi-core computing","volume":"5193","author":"Halperin","year":"2008","journal-title":"Algorithms\u2014Esa 2008"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lenoski, D.E., and Weber, W.-D. (1995). Scalable Shared-Memory Multiprocessing, Morgan Kaufmann Publishers.","DOI":"10.1016\/B978-1-55860-315-8.50010-5"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.cageo.2009.12.008","article-title":"Leveraging the power of multi-core platforms for large-scale geospatial data processing: Exemplified by generating DEM from massive LiDAR point clouds","volume":"36","author":"Guan","year":"2010","journal-title":"Comput. Geosci"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/99.660313","article-title":"Openmp: An industry standard api for shared-memory programming","volume":"5","author":"Dagum","year":"1998","journal-title":"IEEE Comput. Sci. Eng"},{"key":"ref_24","unstructured":"Reinders, J. (2007). Intel Threading Building Blocks: Outfitting C++ for Multi-Core Processor Parallelism, O\u2019Reilly Media, Inc."},{"key":"ref_25","unstructured":"Jayasena, N.S. (2005). Memory Hierarchy Design for Stream Computing, Stanford University."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","article-title":"Mapreduce: Simplified data processing on large clusters","volume":"51","author":"Dean","year":"2008","journal-title":"Commun. ACM"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s002360050095","article-title":"A survey of stream processing","volume":"34","author":"Stephens","year":"1997","journal-title":"Acta Inform"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1137\/S0097539799361701","article-title":"An approximate L1-difference algorithm for massive data streams","volume":"32","author":"Feigenbaum","year":"2002","journal-title":"SIAM J. Comput"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Neumeyer, L., Robbins, B., Nair, A., and Kesari, A. (2010, January 13). S4: Distributed stream computing platform. Sydney, Australia.","DOI":"10.1109\/ICDMW.2010.172"},{"key":"ref_30","unstructured":"Gummaraju, J., and Rosenblum, M. (2004, January 9\u201313). Stream processing in general-purpose processors. Boston, MA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1016\/j.cageo.2011.01.008","article-title":"Parastream: A parallel streaming delaunay triangulation algorithm for LiDAR points on multicore architectures","volume":"37","author":"Wu","year":"2011","journal-title":"Comput. Geosci"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1007\/11863939_13","article-title":"Generating raster dem from mass points via tin streaming","volume":"4197","author":"Raubal","year":"2006","journal-title":"Geographic Information Science"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, Q.-Y., and Neumann, U. (2009, January 20\u201325). A streaming framework for seamless building reconstruction from large-scale aerial LiDAR data. Miami, FL, USA. (CVPR 2009).","DOI":"10.1109\/CVPR.2009.5206760"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.3390\/s90402555","article-title":"Parallel processing method for airborne laser scanning data using a PC cluster and a virtual grid","volume":"9","author":"Han","year":"2009","journal-title":"Sensors"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Krishnan, S., Baru, C., and Crosby, C. (2010, January 30). Evaluation of mapreduce for gridding LiDAR data. Indianapolis, IN, USA.","DOI":"10.1109\/CloudCom.2010.34"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/LGRS.2012.2205130","article-title":"Fast filtering of LiDAR point cloud in urban areas based on scan line segmentation and GPU acceleration","volume":"10","author":"Hu","year":"2013","journal-title":"IEEE Geosci. Remote Sens"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 18). 3D is here: Point Cloud Library (PCL). Shanghai, China. 41\u2013.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5498","DOI":"10.1073\/pnas.0909315108","article-title":"Using spatial principles to optimize distributed computing for enabling the physical science discoveries","volume":"108","author":"Yang","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_39","first-page":"203","article-title":"Filtering of laser altimetry data using a slope adaptive filter","volume":"34","author":"Sithole","year":"2001","journal-title":"ISPRS J. Photogramm. Remote Sens"},{"key":"ref_40","first-page":"227","article-title":"Airborne laser scanning-clustering in raw data","volume":"34","author":"Roggero","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_41","first-page":"336","article-title":"Terrain surface reconstruction by the use of tetrahedron model with the MDL criterion","volume":"34","author":"Sohn","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_42","unstructured":"Brovelli, M., Cannata, M., and Longoni, U. (2002, January 11\u201313). Managing and processing LIDAR data within GRASS. Trento, Italy."},{"key":"ref_43","first-page":"153","article-title":"Interpolation and filtering of laser scanner data-implementation and first results","volume":"32","author":"Pfeifer","year":"1998","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_44","first-page":"114","article-title":"Ground surface estimation from airborne laser scanner data using active shape models","volume":"34","author":"Elmqvist","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1080\/19479832.2012.734339","article-title":"Land cover classification using airborne LiDAR products in beauport, Qu\u00e9bec, Canada","volume":"4","author":"Zhu","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_46","unstructured":"Soininen, A. (2012). Terrascan User\u2019s Guide, The National Mapping Agency of Great Britain."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shi, X., Bao, H., and Zhou, K. (2009, January 16\u201319). Out-of-core multigrid solver for streaming meshes. Yokohama, Japan.","DOI":"10.1145\/1661412.1618519"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/3-540-31619-1_7","article-title":"Parallel mesh generation","volume":"51","author":"Bruaset","year":"2006","journal-title":"Numerical Solution of Partial Differential Equations on Parallel Computers"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0925-7721(96)00025-9","article-title":"A comparison of sequential delaunay triangulation algorithms","volume":"7","author":"Su","year":"1997","journal-title":"Comput. Geom"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, M.C., and Manocha, D. (1996). Applied Computational Geometry Towards Geometric Engineering, Springer-Verlag.","DOI":"10.1007\/BFb0014474"},{"key":"ref_51","first-page":"4","article-title":"Hyper-threading technology architecture and microarchitecture","volume":"6","author":"Marr","year":"2002","journal-title":"Int. Technol. J"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shi, X., Kindratenko, V., and Yang, C. (2013). Modern Accelerator Technologies for Geographic Information Science, Springer.","DOI":"10.1007\/978-1-4614-8745-6"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Rauber, T., and R\u00fcnger, G. (2013). Parallel Programming, Springer.","DOI":"10.1007\/978-3-642-37801-0"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1080\/19479832.2013.804007","article-title":"Texture characterization, representation, description, and classification based on full range Gaussian Markov random field model with Bayesian approach","volume":"4","author":"Seetharaman","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1080\/19479832.2013.824029","article-title":"Feature-based detection using Bayesian data fusion","volume":"4","author":"Akiwowo","year":"2013","journal-title":"Int. J. Image Data Fusion"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/8\/7212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:14:20Z","timestamp":1760217260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/8\/7212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,4]]},"references-count":55,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2014,8]]}},"alternative-id":["rs6087212"],"URL":"https:\/\/doi.org\/10.3390\/rs6087212","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,8,4]]}}}