Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/48187
Título: | Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC |
---|---|
Autor/es: | Saval-Calvo, Marcelo | Azorin-Lopez, Jorge | Fuster-Guilló, Andrés | Garcia-Rodriguez, Jose |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Computer vision | Model extraction | RANSAC multi-plane | Three-dimensional planes |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | sep-2015 |
Editor: | Elsevier |
Cita bibliográfica: | Applied Soft Computing. 2015, 34: 572-586. doi:10.1016/j.asoc.2015.05.007 |
Resumen: | 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 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2015 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1016/j.asoc.2015.05.007 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2015_Saval_etal_Applied-Soft-Computing_final.pdf | Versión final (acceso restringido) | 7,03 MB | Adobe PDF | Abrir Solicitar una copia |
2015_Saval_etal_Applied-Soft-Computing_preprint.pdf | Preprint (acceso abierto) | 6,92 MB | Adobe PDF | Abrir Vista previa |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.