Learning Line Features in 3D Geometry

dc.contributor.authorSunkel, Martinen_US
dc.contributor.authorJansen, Silkeen_US
dc.contributor.authorWand, Michaelen_US
dc.contributor.authorEisemann, Elmaren_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.editorM. Chen and O. Deussenen_US
dc.date.accessioned2015-02-27T10:20:39Z
dc.date.available2015-02-27T10:20:39Z
dc.date.issued2011en_US
dc.description.abstractFeature detection in geometric datasets is a fundamental tool for solving shape matching problems such as partial symmetry detection. Traditional techniques usually employ a priori models such as crease lines that are unspecific to the actual application. Our paper examines the idea of learning geometric features. We introduce a formal model for a class of linear feature constellations based on a Markov chain model and propose a novel, efficient algorithm for detecting a large number of features simultaneously. After a short user-guided training stage, in which one or a few example lines are sketched directly onto the input data, our algorithm automatically finds all pieces of geometry similar to the marked areas. In particular, the algorithm is able recognize larger classes of semantically similar but geometrically varying features, which is very difficult using unsupervised techniques. In a number of experiments, we apply our technique to point cloud data from 3D scanners. The algorithm is able to detect features with very low rates of false positives and negatives and to recognize broader classes of similar geometry (such as "windows" in a building scan) even from few training examples, thereby significantly improving over previous unsupervised techniques.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01858.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleLearning Line Features in 3D Geometryen_US
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