Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

Roman Klokov, Victor Lempitsky

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

    580 Citations (Scopus)

    Abstract

    We present a new deep learning architecture (called Kdnetwork) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture performs multiplicative transformations and shares parameters of these transformations according to the subdivisions of the point clouds imposed onto them by kdtrees. Unlike the currently dominant convolutional architectures that usually require rasterization on uniform twodimensional or three-dimensional grids, Kd-networks do not rely on such grids in any way and therefore avoid poor scaling behavior. In a series of experiments with popular shape recognition benchmarks, Kd-networks demonstrate competitive performance in a number of shape recognition tasks such as shape classification, shape retrieval and shape part segmentation.

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages863-872
    Number of pages10
    ISBN (Electronic)9781538610329
    DOIs
    Publication statusPublished - 22 Dec 2017
    Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
    Duration: 22 Oct 201729 Oct 2017

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    Volume2017-October
    ISSN (Print)1550-5499

    Conference

    Conference16th IEEE International Conference on Computer Vision, ICCV 2017
    Country/TerritoryItaly
    CityVenice
    Period22/10/1729/10/17

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