Classifying Point Clouds at the Facade-Level Using Geometric Features and Deep Learning Networks

Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods’ performance. This method can be applied for compensating deep learning networks’ ability in capturing local geometric information and promoting the advancement of semantic segmentation.

Original languageEnglish
Title of host publicationRecent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference
EditorsThomas H. Kolbe, Andreas Donaubauer, Christof Beil
PublisherSpringer Science and Business Media Deutschland GmbH
Pages391-404
Number of pages14
ISBN (Print)9783031436987
DOIs
StatePublished - 2024
EventInternational 3D GeoInfo Conference, 3DGeoInfo 2023 - Munich, Germany
Duration: 12 Sep 202314 Sep 2023

Publication series

NameLecture Notes in Geoinformation and Cartography
ISSN (Print)1863-2246
ISSN (Electronic)1863-2351

Conference

ConferenceInternational 3D GeoInfo Conference, 3DGeoInfo 2023
Country/TerritoryGermany
CityMunich
Period12/09/2314/09/23

Keywords

  • Deep learning
  • Geometric features
  • Point cloud classification

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