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Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods

Abstract : This paper introduces a new definition of multiscale neighborhoods in 3D point clouds. This definition, based on spherical neighborhoods and proportional subsampling, allows the computation of features with a consistent geometrical meaning, which is not the case when using k-nearest neighbors. With an appropriate learning strategy, the proposed features can be used in a random forest to classify 3D points. In this semantic classification task, we show that our multiscale features outperform state-of-the-art features using the same experimental conditions. Furthermore, their classification power competes with more elaborate classification approaches including Deep Learning methods.
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Contributor : Hugues Thomas <>
Submitted on : Friday, January 18, 2019 - 10:48:44 AM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM

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Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Yann Le Gall. Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods. 2018 International Conference on 3D Vision (3DV), Sep 2018, Verone, Italy. ⟨10.1109/3DV.2018.00052⟩. ⟨hal-01985640⟩



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