In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, i.e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud. To overcome the lack of annotated point cloud data, in particular for the road segments, we introduce an auto-labeling process which transfers automatically generated labels from the camera to LiDAR. We also explore the role of image-like projection of LiDAR data in semantic segmentation by comparing BEV with spherical-front-view projection and show that SalsaNet is projection-agnostic. We perform quantitative and qualitative evaluations on the KITTI dataset, which demonstrate that the proposed SalsaNet outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. Our code and data are publicly available at https://gitlab.com/aksoyeren/salsanet.git.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving


    Contributors:


    Publication date :

    2020-10-19


    Size :

    3500415 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SALSANET: FAST ROAD AND VEHICLE SEGMENTATION IN LIDAR POINT CLOUDS FOR AUTONOMOUS DRIVING

    Aksoy, Eren Erdal / Baci, Saimir / Cavdar, Selcuk | British Library Conference Proceedings | 2020


    Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

    Certad, Novel / Morales-Alvarez, Walter / Olaverri-Monreal, Cristina | IEEE | 2022


    LISEG: LIGHTWEIGHT ROAD-OBJECT SEMANTIC SEGMENTATION IN 3D LIDAR SCANS FOR AUTONOMOUS DRIVING

    Zhang, Wenquan / Zhou, Chancheng / Yang, Junjie et al. | British Library Conference Proceedings | 2018


    LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving

    Zhang, Wenquan / Zhou, Chancheng / Yang, Junjie et al. | IEEE | 2018


    POINT CLOUD SEGMENTATION USING A COHERENT LIDAR FOR AUTONOMOUS VEHICLE APPLICATIONS

    ARMSTRONG-CREWS NICHOLAS / CHEN MINGCHENG / HU XIAOXIANG | European Patent Office | 2022

    Free access