Point Cloud vs. Mesh Features for Building Interior Classification

[en] Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used.

Disciplines :

Computer science
Earth sciences & physical geography
Civil engineering

Author, co-author :

Bassier, Maarten; KU Leuven > Department of Civil Engineering

Vergauwen, Maarten; KU Leuven > Department of Civil Engineering

Poux, Florent ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie

Language :

English

Title :

Point Cloud vs. Mesh Features for Building Interior Classification

Alternative titles :

[fr] Nuage de points vs maillage 3D pour la classification intérieure des bâtiments

Publication date :

11 July 2020

Journal title :

Remote Sensing

eISSN :

2072-4292

Publisher :

MDPI, Basel, Switzerland

Special issue title :

Point Cloud Processing and Analysis in Remote Sensing

Volume :

Issue :

Pages :

2224

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.mdpi.com/2072-4292/12/14/2224
https://learngeodata.eu
https://pointcloudproject.com

European Projects :

H2020 - 779962 - V4Design - Visual and textual content re-purposing FOR(4) architecture, Design and video virtual reality games

Funders :

ERC - European Research Council
VLAIO - Agentschap Innoveren & Ondernemen
ULiège - Université de Liège
KU Leuven - Katholieke Universiteit Leuven
EC - European Commission

Available on ORBi :

since 31 July 2020

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