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Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods / Florent Poux in ISPRS International journal of geo-information, vol 8 n° 5 (May 2019)  

Public [article]  inISPRS International journal of geo-information > vol 8 n° 5 (May 2019) . - n° 213 Titre : Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods Type de document : Article/Communication Auteurs : Florent Poux, Auteur ; Roland Billen, Auteur Année de publication : 2019 Article en page(s) : n° 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre de décision [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] connexité (topologie) [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] voxel Résumé : (auteur) Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning. Numéro de notice : A2019-656 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi8050213 Date de publication en ligne : 07/05/2019 En ligne : https://doi.org/10.3390/ijgi8050213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97890 [article]