A Variational Taxonomy for Surface Reconstruction from Oriented Points

dc.contributor.authorSchroers, Christopheren_US
dc.contributor.authorSetzer, Simonen_US
dc.contributor.authorWeickert, Joachimen_US
dc.contributor.editorThomas Funkhouser and Shi-Min Huen_US
dc.date.accessioned2015-03-03T12:42:55Z
dc.date.available2015-03-03T12:42:55Z
dc.date.issued2014en_US
dc.description.abstractThe problem of reconstructing a watertight surface from a finite set of oriented points has received much attention over the last decades. In this paper, we propose a general higher order framework for surface reconstruction. It is based on the idea that position and normal defined by each oriented point can be used to construct an implicit local description of the unknown surface. On the one hand, this allows us to systematically explain and relate several popular methods, for example implicit moving least squares, smooth signed distance surface reconstruction as well as (screened) Poisson surface reconstruction. On the other hand, it allows to derive and discuss a number of new approaches for reconstructing either the signed distance or the indicator function of the sought object. All of these approaches are able to achieve competitive results but one of them turns out to be especially promising. To improve reconstructions in difficult real world scenarios where point clouds have been estimated from colour images, we introduce a hull constraint that encourages the surface to stay within a given region. Our framework is implemented on the GPU using a recent cyclic scheme called Fast Jacobi, which combines low implementational effort with high efficiency.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12445en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12445en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleA Variational Taxonomy for Surface Reconstruction from Oriented Pointsen_US
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