HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-01985640
Contributor : Hugues Thomas Connect in order to contact the contributor
Submitted on : Friday, January 18, 2019 - 10:48:44 AM
Last modification on : Wednesday, November 17, 2021 - 12:31:05 PM

Links full text

Identifiers

Citation

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⟩

Share

Metrics

Record views

2640