• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    2D-Driven 3D Object Detection in RGB-D Images

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Lahoud, Jean
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2017-12-25
    Online Publication Date
    2017-12-25
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/627233
    
    Metadata
    Show full item record
    Abstract
    In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.
    Citation
    Lahoud J, Ghanem B (2017) 2D-Driven 3D Object Detection in RGB-D Images. 2017 IEEE International Conference on Computer Vision (ICCV). Available: http://dx.doi.org/10.1109/ICCV.2017.495.
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Conference on Computer Vision (ICCV)
    Conference/Event name
    16th IEEE International Conference on Computer Vision, ICCV 2017
    DOI
    10.1109/ICCV.2017.495
    Additional Links
    http://ieeexplore.ieee.org/document/8237757/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2017.495
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2019  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.