State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network architectures across different LiDAR sensor types represents a significant challenge, especially due to sensor specific design choices with regard to network architecture as well as data representation. In this paper we propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types. This represents a significant advantage given the fast-paced development of LiDAR hardware technology. We perform a thorough quantitative cross-sensor analysis of semantic labeling performance in comparison to a state-of-the-art reference method. Our evaluation shows that the proposed architecture is indeed highly portable, yielding an improvement of 10 percentage points in the Intersectionover-Union (IoU) score when compared to the reference approach. Further, the results indicate that the proposed network architecture can provide an efficient way for the automated generation of large-scale training data for novel LiDAR sensor types without the need for extensive manual annotation or multi-modal label transfer.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling


    Beteiligte:
    Piewak, Florian (Autor:in) / Pinggera, Peter (Autor:in) / Zollner, Marius (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    2107439 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Software Portability in Open Architectures

    Mosbeck, R. A. | British Library Conference Proceedings | 2001


    Software portability in open architectures

    Mosbeck, R.A. / Reeve, L.C. / Thedens, J.R. | IEEE | 2001




    LIDAR GROUND PLANE ESTIMATION USING LIDAR SEMANTIC NETWORK

    LIONG VENICE ERIN BAYLON / BEIJBOM OSCAR OLOF | Europäisches Patentamt | 2022

    Freier Zugriff