{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T14:11:04Z","timestamp":1776521464862,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,11]],"date-time":"2019-04-11T00:00:00Z","timestamp":1554940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the intrinsic properties of conventional terrestrial laser scanning technology is the unevenness of its point density over the scene where objects rendered closer to the scanner are more densely covered than the ones far away. This uneven distribution can be amplified as the working range of a laser scanner gets longer. In such case a higher pulse repetition rate (PRR) is applied to the whole scanning area and the scanning time will be dramatically increased. To improve the efficiency of the conventional laser scanning technology, a prototype of adaptive scanning technology, the HRS3D-AS scanner has been developed by Blackmore Sensors and Analytics, Inc. This paper briefly describes the working principles of the adaptive scanner and presents a thorough evaluation on the distributions of the point density in comparison to the conventional scanning. Based on this study, we show that such a new technology can produce a point cloud of more uniform density and less data volume. The overall field scanning time can be reduced by several times compared to the conventional, PRR-fixed scanning. Such properties are expected to significantly simplify the algorithmic development and increase the productivity in data acquisition and processing. The limitations of this new adaptive scanning technology are also discussed in terms of redundant and unresolved details. Finally, recommendations related to the practicing of such adaptive scan are discussed.<\/jats:p>","DOI":"10.3390\/rs11070880","type":"journal-article","created":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T03:46:37Z","timestamp":1555040797000},"page":"880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Uniform Point Density: Evaluation of an Adaptive Terrestrial Laser Scanner"],"prefix":"10.3390","volume":"11","author":[{"given":"Qinghua","family":"Li","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchi","family":"Ma","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Anderson","sequence":"additional","affiliation":[{"name":"Geospatial Research Lab, Corbin Field Station, 15319 Magnetic Lane, Woodford, VA 22580, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9806-6029","authenticated-orcid":false,"given":"James","family":"Curry","sequence":"additional","affiliation":[{"name":"Blackmore Sensors and Analytics, Inc., Bozeman, MT 59718, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1948-9657","authenticated-orcid":false,"given":"Jie","family":"Shan","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shan, J., and Toth, C.K. (2018). Introduction to Laser Ranging, Profiling, and Scanning, Chapter 1. Topographic Laser Ranging and Scanning: Principles and Processing, CRC Press. [2nd ed.].","DOI":"10.1201\/9781315154381"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Heidemann, H.K. (2012). Lidar Base Specification Version 1.0. (USGS Techniques and Methods 11-B4).","DOI":"10.3133\/tm11B3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4652","DOI":"10.3390\/rs5094652","article-title":"Synthesis of transportation applications of mobile LiDAR","volume":"5","author":"Williams","year":"2013","journal-title":"Remote Sens."},{"key":"ref_4","first-page":"1","article-title":"Fast 3D scanning methods for laser measurement systems","volume":"25","author":"Wulf","year":"2003","journal-title":"Int. Conf. Control. Syst. Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Morales, J., Plazaleiva, V., Mandow, A., Gomezruiz, J.A., Ser\u00f3n, J., and Garc\u00edacerezo, A. (2018). Analysis of 3d scan measurement distribution with application to a multi-beam lidar on a rotating platform. Sensors, 18.","DOI":"10.3390\/s18020395"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.032","article-title":"Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size","volume":"198","author":"Roussel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.isprsjprs.2014.12.021","article-title":"Effects of LiDAR point density and landscape context on estimates of urban forest biomass","volume":"101","author":"Singh","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/JSTARS.2016.2522960","article-title":"When big data are too much: Effects of LiDAR returns and point density on estimation of forest biomass","volume":"9","author":"Singh","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13021-017-0073-1","article-title":"Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR","volume":"12","author":"Garcia","year":"2017","journal-title":"Carbon Balance Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"337","DOI":"10.5721\/EuJRS20164919","article-title":"Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns","volume":"49","author":"Kandare","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2012.11.024","article-title":"Tradeoffs between lidar pulse density and forest measurement accuracy","volume":"130","author":"Jakubowski","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","unstructured":"Tomljenovic, I., and Rousell, A. (2014, January 3\u20136). Influence of point cloud density on the results of automated Object-Based building extraction from ALS data. Proceedings of the 17th AGILE Conference on Geographic Information Science, Castellon, Spain."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rozas, E., Rivera, F.F., Cabaleiro, J.C., Pena, T.F., and Vilari\u00f1o, D.L. (2017, January 11\u201313). Comparative study of building footprint estimation methods from LiDAR point clouds. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2280094"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7857","DOI":"10.3390\/rs6097857","article-title":"MIMIC: An innovative methodology for determining mobile laser scanning system point density","volume":"6","author":"Cahalane","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","first-page":"362","article-title":"Point Distribution as True Quality of LiDAR Point Cloud","volume":"5","author":"Kodors","year":"2017","journal-title":"Balt. J. Mod. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.patrec.2010.03.008","article-title":"Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration","volume":"31","author":"Mandow","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2300000035","article-title":"A review of point cloud registration algorithms for mobile robotics","volume":"4","author":"Pomerleau","year":"2015","journal-title":"Found. Trends Robot."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast point feature histograms (FPFH) for 3D registration. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1002\/rob.21543","article-title":"Automatic calibration of spinning actuated lidar internal parameters","volume":"32","author":"Alismail","year":"2015","journal-title":"J. Field Robot."},{"key":"ref_21","first-page":"177","article-title":"Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals of Photogrammetry","volume":"3","author":"Hackel","year":"2016","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","first-page":"W3","article-title":"A review of point clouds segmentation and classification algorithms. Int. Arch. Photogramm","volume":"42","author":"Grilli","year":"2017","journal-title":"Remote Sens. Spatial Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.04.022","article-title":"Robust point cloud classification based on multi-level semantic relationships for urban scenes","volume":"129","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Neumann, T., D\u00fclberg, E., Schiffer, S., and Ferrein, A. (2016). A Rotating Platform for Swift Acquisition of Dense 3D Point Clouds. International Conference on Intelligent Robotics and Applications, Springer.","DOI":"10.1007\/978-3-319-43506-0_22"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.procir.2015.12.141","article-title":"On the trade-off between data density and data capture duration in 3D laser scanning for production system engineering","volume":"41","author":"Berglund","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_26","first-page":"233","article-title":"A comparative study between frequency-modulated continuous wave LADAR and linear LiDAR. The International Archives of Photogrammetry","volume":"40","author":"Massaro","year":"2014","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.83.10.721","article-title":"LADAR: Frequency-Modulated, Continuous Wave LAser Detection and Ranging","volume":"83","author":"Anderson","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/880\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:44:39Z","timestamp":1760186679000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,11]]},"references-count":27,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070880"],"URL":"https:\/\/doi.org\/10.3390\/rs11070880","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,11]]}}}