Anatomically Plausible Surface Alignment and Reconstruction

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    With the increasing clinical use of 3D surface scanners, there is a need for accurate and reliable algorithms that can produce anatomically plausible surfaces. In this paper, a combined method for surface alignment and reconstruction is proposed. It is based on an implicit surface representation combined with a Markov Random Field regularisation method. Conceptually, the method maintains an implicit ideal description of the sought surface. This implicit surface is iteratively updated by realigning the input point sets and Markov Random Field regularisation. The regularisation is based on a prior energy that has earlier proved to be particularly well suited for human surface scans. The method has been tested on full cranial scans of ten test subjects and on several scans of the outer human ear.
    Original languageEnglish
    Title of host publicationEurographics UK conference on Theory and Practice of Computer Graphics.
    Publication date2010
    Publication statusPublished - 2010
    EventTheory and Practice of Computer Graphics - Sheffield, United Kingdom
    Duration: 6 Sep 20108 Sep 2010
    Conference number: 8


    ConferenceTheory and Practice of Computer Graphics
    Country/TerritoryUnited Kingdom

    Bibliographical note

    Best paper award at TPCG'2010


    • Surface reconstruction, Markov Random Field, Surface Alignment

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