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A Saliency-Based Sparse Representation Method for Point Cloud Simplification

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 simplifi...

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Autores principales: Leal, Esmeide, Sanchez-Torres, German, Branch-Bedoya, John W., Abad, Francisco, Leal, Nallig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271750/
https://www.ncbi.nlm.nih.gov/pubmed/34201455
http://dx.doi.org/10.3390/s21134279
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author Leal, Esmeide
Sanchez-Torres, German
Branch-Bedoya, John W.
Abad, Francisco
Leal, Nallig
author_facet Leal, Esmeide
Sanchez-Torres, German
Branch-Bedoya, John W.
Abad, Francisco
Leal, Nallig
author_sort Leal, Esmeide
collection PubMed
description 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.
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spelling pubmed-82717502021-07-11 A Saliency-Based Sparse Representation Method for Point Cloud Simplification Leal, Esmeide Sanchez-Torres, German Branch-Bedoya, John W. Abad, Francisco Leal, Nallig Sensors (Basel) Article 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. MDPI 2021-06-23 /pmc/articles/PMC8271750/ /pubmed/34201455 http://dx.doi.org/10.3390/s21134279 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Leal, Esmeide
Sanchez-Torres, German
Branch-Bedoya, John W.
Abad, Francisco
Leal, Nallig
A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title_full A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title_fullStr A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title_full_unstemmed A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title_short A Saliency-Based Sparse Representation Method for Point Cloud Simplification
title_sort saliency-based sparse representation method for point cloud simplification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271750/
https://www.ncbi.nlm.nih.gov/pubmed/34201455
http://dx.doi.org/10.3390/s21134279
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