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    Indoor 3D Scene Understanding Using Depth Sensors

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    JeanLahoudThesis.pdf
    Size:
    22.25Mb
    Format:
    PDF
    Description:
    Final Thesis
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    Type
    Dissertation
    Authors
    Lahoud, Jean cc
    Advisors
    Ghanem, Bernard cc
    Committee members
    Heidrich, Wolfgang cc
    Wonka, Peter cc
    Cremers, Daniel
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2020-09
    Permanent link to this record
    http://hdl.handle.net/10754/665033
    
    Metadata
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    Abstract
    One of the main goals in computer vision is to achieve a human-like understanding of images. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.
    Citation
    Lahoud, J. (2020). Indoor 3D Scene Understanding Using Depth Sensors. KAUST Research Repository. https://doi.org/10.25781/KAUST-8L876
    DOI
    10.25781/KAUST-8L876
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-8L876
    Scopus Count
    Collections
    PhD Dissertations; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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