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    Smooth Kernel Density Estimate for Multiple View Reconstruction


    Ruttle, Jonathan and Manzke, Michael and Dahyot, Rozenn (2010) Smooth Kernel Density Estimate for Multiple View Reconstruction. proceedings of The 7th European Conference for Visual Media Production, CVMP 2010.

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    Abstract

    We present a statistical framework to merge the information from silhouettes segmented in multiple view images to infer the 3D shape of an object. The approach is generalising the robust but discrete modelling of the visual hull by using the concept of averaged likelihoods. One resulting advantage of our framework is that the objective function is continuous and therefore an iterative gradient ascent algorithm can be defined to efficiently search the space. Moreover this results in a method which is less memory demanding and one that is very suitable to a parallel processing architecture. Experimental results shows that this approach is efficient for getting a robust initial guess to the 3D shape of an object in view.

    Item Type: Article
    Keywords: Shape from silhouette; Kernel Density estimate; Newton-Raphson;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15282
    Identification Number: https://doi.org/10.1109/CVMP.2010.17
    Depositing User: Rozenn Dahyot
    Date Deposited: 19 Jan 2022 12:24
    Journal or Publication Title: proceedings of The 7th European Conference for Visual Media Production, CVMP 2010
    Refereed: Yes
    URI:

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