Unsupervised learning of 3D object models from partial views

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 model...

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Bibliographic Details
Published in2009 IEEE International Conference on Robotics and Automation pp. 801 - 806
Main Authors Ruhnke, M., Steder, B., Grisetti, G., Burgard, W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2009
Subjects
Clouds
Iterative algorithms
Laser modes
Layout
Merging
model learning
Object detection
range images
Robotics and automation
Robots
Robustness
Unsupervised learning
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ISBN1424427886
9781424427888
ISSN1050-4729
DOI10.1109/ROBOT.2009.5152524

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Summary: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.
ISBN:1424427886
9781424427888
ISSN:1050-4729
DOI:10.1109/ROBOT.2009.5152524