Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/48187
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Title: Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
Authors: Saval-Calvo, Marcelo | Azorin-Lopez, Jorge | Fuster-Guilló, Andrés | Garcia-Rodriguez, Jose
Research Group/s: Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Computer vision | Model extraction | RANSAC multi-plane | Three-dimensional planes
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Sep-2015
Publisher: Elsevier
Citation: Applied Soft Computing. 2015, 34: 572-586. doi:10.1016/j.asoc.2015.05.007
Abstract: Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.
URI: http://hdl.handle.net/10045/48187
ISSN: 1568-4946 (Print) | 1872-9681 (Online)
DOI: 10.1016/j.asoc.2015.05.007
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 Elsevier B.V.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.asoc.2015.05.007
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - AIA - Artículos de Revistas

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