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    A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks

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    Name:
    HaniItani_MSThesis.pdf
    Size:
    21.29Mb
    Format:
    PDF
    Description:
    Final Thesis
    Embargo End Date:
    2021-11-11
    Download
    Type
    Thesis
    Authors
    Itani, Hani cc
    Advisors
    Ghanem, Bernard cc
    Committee members
    Ghanem, Bernard cc
    Al-Naffouri, Tareq Y. cc
    Wonka, Peter cc
    Thabet, Ali K.
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-11
    Embargo End Date
    2021-11-11
    Permanent link to this record
    http://hdl.handle.net/10754/665898
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-11-11.
    Abstract
    Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric rep- resentation of scenes and tangible objects. Convolutional Neural Networks (CNNs) have demonstrated an impressive success in processing regular discrete data such as 2D images and 1D audio. However, CNNs do not directly generalize to point cloud processing due to their irregular and un-ordered nature. One way to extend CNNs to point cloud understanding is to derive an intermediate euclidean representation of a point cloud by projecting onto image domain, voxelizing, or treating points as vertices of an un-directed graph. Graph-CNNs (GCNs) have demonstrated to be a very promising solution for deep learning on irregular data such as social networks, bi- ological systems, and recently point clouds. Early works in literature for graph based point networks relied on constructing dynamic graphs in the node feature space to define a convolution kernel. Later works constructed hierarchical static graphs in 3D space for an encoder-decoder framework inspired from image segmentation. This thesis takes a closer look at both dynamic and static graph neighborhoods of graph- based point networks for the task of semantic segmentation in order to: 1) discuss a potential cause for why going deep in dynamic GCNs does not necessarily lead to an improved performance, and 2) propose a new approach in treating points in a static graph neighborhood for an improved information aggregation. The proposed method leads to an efficient graph based 3D semantic segmentation network that is on par with current state-of-the-art methods on both indoor and outdoor scene semantic segmentation benchmarks such as S3DIS and Semantic3D.
    Citation
    Itani, H. (2020). A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks. KAUST Research Repository. https://doi.org/10.25781/KAUST-K7R09
    DOI
    10.25781/KAUST-K7R09
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-K7R09
    Scopus Count
    Collections
    Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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