Improving Local Symmetry Estimations in RGB-D Images by Fitting Superquadrics
Use este enlace para citar
http://hdl.handle.net/2183/29569
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-CompartirIgual 4.0 Internacional
Colecciones
Metadatos
Mostrar el registro completo del ítemTítulo
Improving Local Symmetry Estimations in RGB-D Images by Fitting SuperquadricsFecha
2016Cita bibliográfica
Fornas, D., Sanz, P.J., Porta, J.M., Thomas F. Improving local symmetry estimations in RGB-D images by fitting superquadrics. En Actas de las XXXVII Jornadas de Automática. 7, 8 y 9 de septiembre de 2016, Madrid (pp. 162-168). DOI capítulo: https://doi.org/10.17979/spudc.9788497498081.0162 DOI libro: https://doi.org/10.17979/spudc.9788497498081
Resumen
[Abstract] Real-time manipulation tasks rely on finding good candidates for apprehension points which, in turn, usually requires the computation of local symmetries. When RGB-D images are used as input information, these local symmetries can be deduced from segmenting these images and computing geometric moments for each cluster of points. This approach gives a rough approximation because it does not take into account that the considered points lie on a surface. In this paper, to improve the quality of the symmetry estimations, we propose a simple refinement process that takes as input the estimation obtained using moments and then fits a superquadric to the considered set of points. We evaluate our approach on data collected using a Microsoft's Kinect 2 sensor. The obtained experimental results demonstrate the efficacy of the proposed approach.
Palabras clave
Symmetry detection
Point clouds
Object Segmentation
Superquadrics
Kinect sensor
Point clouds
Object Segmentation
Superquadrics
Kinect sensor
Versión del editor
Derechos
Atribución-NoComercial-CompartirIgual 4.0 Internacional
ISBN
978-84-617-4298-1 (UCM) 978-84-9749-808-1 (UDC electrónico)