DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications

Li Li, Khalid N. Ismail, Hubert P.H. Shum, Toby P. Breckon

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Citations (SciVal)
    Original languageEnglish
    Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1227-1237
    Number of pages11
    ISBN (Electronic)9781665426886
    DOIs
    Publication statusPublished (VoR) - 2021
    Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
    Duration: 1 Dec 20213 Dec 2021

    Publication series

    NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021

    Conference

    Conference9th International Conference on 3D Vision, 3DV 2021
    Country/TerritoryUnited Kingdom
    CityVirtual, Online
    Period1/12/213/12/21

    Keywords

    • autonomous driving
    • dataset
    • dense depth
    • flash LiDAR
    • ground truth depth
    • high resolution LiDAR
    • monocular depth estimation
    • stereo vision
    • three dimensional

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