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.
@article{arxiv.1808.00495,
title = {Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods},
author = {Hugues Thomas and Jean-Emmanuel Deschaud and Beatriz Marcotegui and François Goulette and Yann Le Gall},
journal= {arXiv preprint arXiv:1808.00495},
year = {2018}
}