{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:33:01Z","timestamp":1770697981429,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007637","name":"Departamento Administrativo de Ciencia, Tecnolog\u00eda e Innovaci\u00f3n (COLCIENCIAS)","doi-asserted-by":"publisher","award":["2015727"],"award-info":[{"award-number":["2015727"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.<\/jats:p>","DOI":"10.3390\/s21134279","type":"journal-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T03:22:00Z","timestamp":1624418520000},"page":"4279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Saliency-Based Sparse Representation Method for Point Cloud Simplification"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2468-370X","authenticated-orcid":false,"given":"Esmeide","family":"Leal","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00edas, Universidad Aut\u00f3noma del Caribe, Barranquilla 080001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9069-0732","authenticated-orcid":false,"given":"German","family":"Sanchez-Torres","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00edas, Universidad del Magdalena, Santa Marta 470004, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John W.","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia-Sede Medell\u00edn, Medell\u00edn 050041, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Abad","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Autom\u00e1tica e Inform\u00e1tica Industrial, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nallig","family":"Leal","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00edas, Universidad Aut\u00f3noma del Caribe, Barranquilla 080001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Levoy, M., Ginsberg, J., Shade, J., Fulk, D., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., and Ginzton, M. 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