A Voxelized Fractal Descriptor for 3D Object Recognition
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/109086
Título: | A Voxelized Fractal Descriptor for 3D Object Recognition |
---|---|
Autor/es: | Domenech, Jose Francisco | Escalona, Félix | Gomez-Donoso, Francisco | Cazorla, Miguel |
Grupo/s de investigación o GITE: | Robótica y Visión Tridimensional (RoViT) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | 3D object recognition | Fractal | Global descriptor | Machine learning |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 3-sep-2020 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Access. 2020, 8: 161958-161968. https://doi.org/10.1109/ACCESS.2020.3021455 |
Resumen: | Currently, state-of-the-art methods for 3D object recognition rely in a deep learning-pipeline. Nonetheless, these methods require a large amount of data that is not easy to obtain. In addition to that, the majority of them exploit features of the datasets, like the fact of being CAD models to create rendered representation which will not work in real life because the 3D sensors provide point clouds. We propose a novel global descriptor for point clouds which takes advantage of the fractal dimension of the objects. Our approach introduces many benefits, such as being agnostic to the density of points of the sample, number of points in the input cloud, sensor of choice, and noise up to a level, and it works on real life point cloud data provided by commercial sensors. We tested our descriptor for 3D object recognition using ModelNet, which is a well-known dataset for that task. Our approach achieves 92.84% accuracy on the ModelNet10, and 88.74% accuracy on the ModelNet40. |
Patrocinador/es: | This work was supported in part by the Spanish Government, with Feder funds, under Grant PID2019-104818RB-I00, and in part by the Spanish Grants for Ph.D. studies under Grant ACIF/2017/243 and Grant FPU16/00887. |
URI: | http://hdl.handle.net/10045/109086 |
ISSN: | 2169-3536 |
DOI: | 10.1109/ACCESS.2020.3021455 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/ACCESS.2020.3021455 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Domenech_etal_2020_IEEEAccess.pdf | 2,17 MB | Adobe PDF | Abrir Vista previa | |
Este ítem está licenciado bajo Licencia Creative Commons