The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 309–316, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-309-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 309–316, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-309-2020

  12 Aug 2020

12 Aug 2020

SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD

F. Poux1 and J. J. Ponciano2 F. Poux and J. J. Ponciano
  • 1Geomatics Unit, University of Liège (ULiege), Belgium
  • 2i3mainz, University of Applied Sciences Mainz, Germany

Keywords: 3D point cloud, voxel, feature extraction, instance segmentation, classification, 3D semantics, ontology, deep learning

Abstract. Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.