@inproceedings{88c87ab5d2f945e582f963a2489b14b4,
title = "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.",
keywords = "3D, Classification, Features, Learning, Multiscale, Neighborhoods, Point Clouds, Random Forest, Segmentation, Semantic",
author = "Hugues Thomas and Francois Goulette and Deschaud, \{Jean Emmanuel\} and Beatriz Marcotegui and Gall, \{Yann Le\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on 3D Vision, 3DV 2018 ; Conference date: 05-09-2018 Through 08-09-2018",
year = "2018",
month = oct,
day = "12",
doi = "10.1109/3DV.2018.00052",
language = "English",
series = "Proceedings - 2018 International Conference on 3D Vision, 3DV 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "390--398",
booktitle = "Proceedings - 2018 International Conference on 3D Vision, 3DV 2018",
}