{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:06:17Z","timestamp":1783163177890,"version":"3.54.6"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,15]],"date-time":"2017-03-15T00:00:00Z","timestamp":1489536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007273","name":"Comisi\u00f3n Interministerial de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["DPI2015-65186-R"],"award-info":[{"award-number":["DPI2015-65186-R"]}],"id":[{"id":"10.13039\/501100007273","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002878","name":"Consejer\u00eda de Econom\u00eda, Innovaci\u00f3n, Ciencia y Empleo, Junta de Andaluc\u00eda","doi-asserted-by":"publisher","award":["P10-TEP-6101-R"],"award-info":[{"award-number":["P10-TEP-6101-R"]}],"id":[{"id":"10.13039\/501100002878","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.<\/jats:p>","DOI":"10.3390\/s17030594","type":"journal-article","created":{"date-parts":[[2017,3,15]],"date-time":"2017-03-15T11:39:26Z","timestamp":1489577966000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Victoria","family":"Plaza-Leiva","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Ingenier\u00eda de Sistemas y Autom\u00e1tica, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7469-8112","authenticated-orcid":false,"given":"Jose","family":"Gomez-Ruiz","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Ingenier\u00eda de Sistemas y Autom\u00e1tica, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9994-6239","authenticated-orcid":false,"given":"Anthony","family":"Mandow","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Ingenier\u00eda de Sistemas y Autom\u00e1tica, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3432-3230","authenticated-orcid":false,"given":"Alfonso","family":"Garc\u00eda-Cerezo","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Ingenier\u00eda de Sistemas y Autom\u00e1tica, Andaluc\u00eda Tech, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21931","DOI":"10.3390\/s150921931","article-title":"A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment","volume":"15","author":"Liu","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Menna, M., Gianni, M., Ferri, F., and Pirri, F. 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