We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models.© 2009 IEEE.
Unsupervised learning of 3D object models from partial views / M., Ruhnke; B., Steder; Grisetti, Giorgio; W., Burgard. - (2009), pp. 801-806. (Intervento presentato al convegno 2009 IEEE International Conference on Robotics and Automation, ICRA '09 tenutosi a Kobe) [10.1109/robot.2009.5152524].
Unsupervised learning of 3D object models from partial views
GRISETTI, GIORGIO;
2009
Abstract
We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models.© 2009 IEEE.File | Dimensione | Formato | |
---|---|---|---|
VE_2009_11573-218594.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
800.04 kB
Formato
Adobe PDF
|
800.04 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.