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, 369–373, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-369-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 369–373, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-369-2020

  12 Aug 2020

12 Aug 2020

INDOOR 3D POINT CLOUDS SEMANTIC SEGMENTATION BASES ON MODIFIED POINTNET NETWORK

J. Zhao1,2,3, X. Zhang1,3, and Y. Wang1,3 J. Zhao et al.
  • 1School of Geomatics and Urban Information, Beijing University of Civil Engineering and Architecture, 102616 Beijing, China
  • 2Key laboratory of Modern Urban Surveying and Mapping, National Administration of Surveying, Mapping and Geoinformation, 102616 Beijing, China
  • 3Beijing Key Laboratory For Architectural Heritage Fine Reconstruction & Health Monitoring, 102616 Beijing, China

Keywords: 3D LiDAR Point Cloud, Point Cloud Segmentation, Semantic Segmentation, Deep Learning, Indoor Structural Elements, PointNet

Abstract. Indoor 3D point clouds semantics segmentation is one of the key technologies of constructing 3D indoor models,which play an important role on domains like indoor navigation and positioning,intelligent city, intelligent robot etc. The deep-learning-based methods for point cloud segmentation take on higher degree of automation and intelligence. PointNet,the first deep neural network which manipulate point cloud directly, mainly extracts the global features but lacks of learning and extracting local features,which causes the poor ability of segmenting the local details of architecture and affects the precision of structural elements segmentation . Focusing on the problems above,this paper put forward an automatic end-to-end segmentation method base on the modified PointNet. According to the characteristic that the intensity of different indoor structural elements differ a lot, we input the point cloud information of 3D coordinate, color and intensity into the feature space of points. Also,a MaxPooling is added into the original PointNet network to improve the ability of attracting and learning local features. In addition, replace the 1×1 convolution kernel of original PointNet with 3×3 convolution kernel in the process of attracting features to improve the segmentation precision of indoor point cloud. The result shows that this method improves the automation and precision of indoor point cloud segmentation for the precision achieves over 80% to segment the structural elements like wall,door and so on ,and the average segmentation precision of every structural elements achieves 66%.