A method and system provide for temporal fusion of depth maps in an image space representation. A series of depth maps are obtained/acquired from one or more depth sensors at a first time. A first Gaussian mixture model (GMM) is initialized using one of the series of depth maps. A second depth map is obtained from the depth sensors at a second time. An estimate of the motion of the depth sensors, from the first time to the second time, is received. A predictive GMM at the second time is created based on a transform of the first GMM and the estimate of the motion. The predictive GMM is updated based on the second depth map.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Gaussian mixture models for temporal depth fusion


    Beteiligte:
    MATTHIES LARRY H (Autor:in) / CIGLA CEVAHIR (Autor:in)

    Erscheinungsdatum :

    04.10.2022


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G06V / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / B64C AEROPLANES , Flugzeuge / G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL / B64D Ausrüstung für Flugzeuge , EQUIPMENT FOR FITTING IN OR TO AIRCRAFT / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



    GAUSSIAN MIXTURE MODELS FOR TEMPORAL DEPTH FUSION

    MATTHIES LARRY H / CIGLA CEVAHIR | Europäisches Patentamt | 2018

    Freier Zugriff

    Probabilistic trajectory prediction with Gaussian mixture models

    Wiest, Jurgen / Hoffken, Matthias / Kresel, Ulrich et al. | IEEE | 2012


    Probabilistic Trajectory Prediction with Gaussian Mixture Models

    Wiest, J. / Hoffken, M. / Kressel, U. et al. | British Library Conference Proceedings | 2012


    Gaussian Mixture Models for Parking Demand Data

    Fiez, Tanner / Ratliff, Lillian J. | IEEE | 2020


    Revisiting Gaussian Mixture Models for Driver Identification

    Jafatnejad, Sasan / Castignani, German / Engel, Thomas | IEEE | 2018