High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection. Upsampling is often based on combining data from a monocular camera to compensate the low-resolution of a LIDAR. This paper, on the other hand, introduces a novel framework to obtain dense depth-map solely from a single LIDAR point cloud; which is a research direction that has been barely explored. The formulation behind the proposed depth-mapping process relies on local spatial interpolation, using sliding-window (mask) technique, and on the Bilateral Filter (BF) where the variable of interest, the distance from the sensor, is considered in the interpolation problem. In particular, the BF is conveniently modified to perform depth-map upsampling such that the edges (foreground-background discontinuities) are better preserved by means of a proposed method which influences the range-based weighting term. Other methods for spatial upsampling are discussed, evaluated and compared in terms of different error measures. This paper also researches the role of the mask's size in the performance of the implemented methods. Quantitative and qualitative results from experiments on the KITTI Database, using LIDAR point clouds only, show very satisfactory performance of the approach introduced in this work.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    High-resolution LIDAR-based depth mapping using bilateral filter


    Beteiligte:
    Premebida, Cristiano (Autor:in) / Garrote, Luis (Autor:in) / Asvadi, Alireza (Autor:in) / Ribeiro, A. Pedro (Autor:in) / Nunes, Urbano (Autor:in)


    Erscheinungsdatum :

    2016-11-01


    Format / Umfang :

    1301557 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    High-Precision Depth Estimation Using Uncalibrated LiDAR and Stereo Fusion

    Park, Kihong / Kim, Seungryong / Sohn, Kwanghoon | IEEE | 2020


    Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds

    Dimitrievski, Martin / Veelaert, Peter / Philips, Wilfried | IEEE | 2017


    Mapping using GIS, LiDAR and surveying

    Simmons-Boardman Publishing Corporation 345 Hudson St., 12th Floor USA - 10014 New York | IuD Bahn | 2008


    Sparse Pseudo-LiDAR Depth Assisted Monocular Depth Estimation

    Shao, Shuwei / Pei, Zhongcai / Chen, Weihai et al. | IEEE | 2024