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dc.contributor.authorSeversky, Lee M.en_US
dc.contributor.authorYin, Lijunen_US
dc.contributor.editorC. Bregler, P. Sander, and M. Wimmeren_US
dc.description.abstractShapes with complex geometric and topological features such as tunnels, neighboring sheets, and cavities are susceptible to undersampling and continue to challenge existing reconstruction techniques. In this work we introduce a new measure for point clouds to determine the likely interior and exterior regions of an object. Specifically, we adapt the concept of parity to point clouds with missing data and introduce the parity map, a global measure of parity over the volume. We first examine how parity changes over the volume with respect to missing data and develop a method for extracting topologically correct interior and exterior crusts for estimating a signed distance field and performing surface reconstruction. We evaluate our approach on real scan data representing complex shapes with missing data. Our parity measure is not only able to identify highly confident interior and exterior regions but also localizes regions of missing data. Our reconstruction results are compared to existing methods and we show that our method faithfully captures the topology and geometry of complex shapes in the presence of missing data.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleA Global Parity Measure for Incomplete Point Cloud Dataen_US
dc.description.seriesinformationComputer Graphics Forumen_US

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  • 31-Issue 7
    Pacific Graphics 2012 - Special Issue

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