Panoptic segmentation is one of the most challenging scene parsing tasks, combining the tasks of semantic segmentation and instance segmentation. While much progress has been made, few works focus on the real-time application of panoptic segmentation methods. In this paper, we revisit the recently introduced K-Net architecture. We propose vital changes to the architecture, training, and inference procedure, which massively decrease latency and improve performance. Our resulting RT-K-Net sets a new state-of-the-art performance for real-time panoptic segmentation methods on the Cityscapes dataset and shows promising results on the challenging Mapillary Vistas dataset. On Cityscapes, RT-K-Net reaches 60.2 % PQ with an average inference time of 32 ms for full resolution 1024×2048 pixel images on a single Titan RTX GPU. On Mapillary Vistas, RT-K-Net reaches 33.2 % PQ with an average inference time of 69 ms. Source code is available at https://github.com/markusschoen/RT-K-Net.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation


    Contributors:


    Publication date :

    2023-06-04


    Size :

    8166406 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    INFRASTRUCTURE ANALYSIS USING PANOPTIC SEGMENTATION

    SCHULTER SAMUEL / GARG SPARSH | European Patent Office | 2023

    Free access

    GenPa-SLAM: Using a General Panoptic Segmentation for a Real-Time Semantic Landmark SLAM

    Beer, Lukas / Luettel, Thorsten / Wuensche, Hans-Joachim | IEEE | 2022


    Single Network Panoptic Segmentation for Street Scene Understanding

    de Geus, Daan / Meletis, Panagiotis / Dubbelman, Gijs | IEEE | 2019


    Multi-task Network for Panoptic Segmentation in Automated Driving

    Petrovai, Andra / Nedevschi, Sergiu | IEEE | 2019