Snow corrupts LiDAR point clouds with scattered noise points and false objects, posing a serious threat to the perception of autonomous driving systems. Existing effective point cloud de-snow methods are mainly based on outlier filters that rigidly remove isolated points. There are deep-learning and algorithm-based weather models that can handle adverse conditions such as rain and fog, but snow conditions are rarely considered. In this study, we propose a LiDAR point cloud translation model based on refined generative adversarial networks (GANs) that is not only able to de-noise snow in point clouds but also to generate fake snow points on clear data. Our model is trained on depth image representations of point clouds from unpaired datasets, with a customized loss function for grayscale depth images that can maintain scale consistency. A pixel-wise discriminator structure is designed to improve the de-snowing effect around the ego vehicle. The proposed model expresses a better feature capture on snow in LiDAR point clouds, and experiment results show high-quality snow removal performance on both the scattered and clustered snow points, as well as satisfactory fake snow generation on clear road point clouds.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    LiDAR Point Cloud Translation Between Snow and Clear Conditions Using Depth Images and GANs


    Beteiligte:
    Zhang, Yuxiao (Autor:in) / Ding, Ming (Autor:in) / Yang, Hanting (Autor:in) / Niu, Yingjie (Autor:in) / Feng, Yan (Autor:in) / Ge, Maoning (Autor:in) / Carballo, Alexander (Autor:in) / Takeda, Kazuya (Autor:in)


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    10754187 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Synthesizing Realistic Snow Effects in Driving Images Using GANs and Real Data with Semantic Guidance*

    Yang, Hanting / Ding, Ming / Carballo, Alexander et al. | IEEE | 2023


    IMPROVING LIDAR POINT CLOUD SEGMENTATION USING BOX PREDICTION

    ZHOU LUBING / MENG XIAOLI / SHETTI KARAN RAJENDRA | Europäisches Patentamt | 2024

    Freier Zugriff

    IMPROVING LIDAR POINT CLOUD SEGMENTATION USING BOX PREDICTION

    ZHOU LUBING / MENG XIAOLI / SHETTI KARAN RAJENDRA | Europäisches Patentamt | 2022

    Freier Zugriff

    3D LiDAR Point Cloud Processing Algorithms

    Bondy, Bradford Scott | BASE | 2020

    Freier Zugriff

    Snow Scoop Helps Clear Rear of Plow

    Madden, B. | British Library Online Contents | 1994