Learning Geometric Primitives in Point Clouds

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dc.contributor.authorM. Caputoen_US
dc.contributor.authorK. Denkeren_US
dc.contributor.authorM. Franzen_US
dc.contributor.authorP. Laubeen_US
dc.contributor.authorG. Umlaufen_US
dc.contributor.editorThomas Funkhouser and Shi-Min Huen_US
dc.date.accessioned2015-06-05T07:06:44Z
dc.date.available2015-06-05T07:06:44Z
dc.date.issued2014en_US
dc.description.abstractPrimitive recognition in 3D point clouds is an important aspect in reverse engineering. We propose a method for primitive recognition based on machine learning approaches. The machine learning approaches used for the classification are linear discriminant analysis (LDA) and multi-class support vector machines (SVM). For the classification process local geometric properties (features) of the point cloud are computed based on point relations, normals, and principal curvatures. For the training phase point clouds are generated using a simulation of a laser scanning device based on ray tracing with an error model. The classification rates of novel, curvaturebased geometric features are compared to known geometric features to prove the effectiveness of the approach.en_US
dc.description.seriesinformationSymposium on Geometry Processing 2014 - Postersen_US
dc.identifier.isbn-en_US
dc.identifier.issn-en_US
dc.identifier.urihttps://doi.org/10.2312/sgp20141385en_US
dc.publisherThe Eurographics Associationen_US
dc.titleLearning Geometric Primitives in Point Cloudsen_US
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SGP14: Eurographics Symposium on Geometry Processing - Posters