Point cloud semantic segmentation helps Intelligent Transportation Systems understand traffic scenes by assigning semantic label to each point in the point cloud, and it relies on large amounts of annotated training data. Nevertheless, manually annotating large-scale datasets of complex traffic scenes is quite time-consuming and tedious. This paper proposes INF-PCA, an interactive point cloud semantic annotation method based on implicit neural field, which allows users to achieve high-quality, large-scene and fast-response semantic annotation with only a few dozen mouse clicks. Firstly, the appearance, geometry and semantics of the point clouds are jointly represented by an implicit neural field, which maps a 3D spatial coordinate to its corresponding attributes. Secondly, an uncertainty-based semantic entropy loss and a supervoxel-based local consistency loss are designed to force the network to produce deterministic predictions with local consistency, thus generating smoother and more accurate boundaries. Furthermore, an active learning-based strategy for click-free annotation is proposed and analyzed to further reduce annotation pressure. Comprehensive experiments on multiple datasets including the road scene dataset Toronto3D revealed that INF-PCA can achieve more accurate annotations with faster response speed and only half of the clicks employed by the state-of-the-art methods, and that INF-PCA can be directly applied to intelligent transportation applications such as interactive segmentation of road scenes, inventory of transportation infrastructure assets, and production of high-definition map.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    INF-PCA: Implicit Neural Field-Based Interactive Point Cloud Semantic Annotation


    Beteiligte:
    Liu, Chong (Autor:in) / Han, Xu (Autor:in) / Huang, Weihong (Autor:in) / Long, Chen (Autor:in) / Wang, Wang (Autor:in) / Dong, Zhen (Autor:in) / Yang, Bisheng (Autor:in)


    Erscheinungsdatum :

    01.02.2025


    Format / Umfang :

    11543605 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    SUSTECH POINTS: A PORTABLE 3D POINT CLOUD INTERACTIVE ANNOTATION PLATFORM SYSTEM

    Li, E. / Wang, Shuaijun / Li, Chengyang et al. | British Library Conference Proceedings | 2020


    SUSTech POINTS: A Portable 3D Point Cloud Interactive Annotation Platform System

    Li, E / Wang, Shuaijun / Li, Chengyang et al. | IEEE | 2020


    Point cloud annotation for a warehouse environment

    ECKMAN CHRISTOPHER FRANK / ZHANG ALEXANDER MING | Europäisches Patentamt | 2023

    Freier Zugriff

    Point cloud annotation for a warehouse environment

    ECKMAN CHRISTOPHER FRANK / ZHANG ALEXANDER MING | Europäisches Patentamt | 2022

    Freier Zugriff

    Point cloud annotation for a warehouse environment

    ECKMAN CHRISTOPHER FRANK / ZHANG ALEXANDER MING | Europäisches Patentamt | 2024

    Freier Zugriff