Title Variational surface interpolation from sparse point and normal data
Authors Jan Erik Solem, Henrik Aanaes, Anders Heyden
Alternative Location http://dx.doi.org/10.1109/T..., Restricted Access
Publication IEEE Transactions on Pattern Analysis and Machine Intelligence
Year 2007
Volume 29
Issue 1
Pages 181 - 184
Document type Article
Status Published
Quality controlled Yes
Language eng
Publisher IEEE Computer Society
Abstract English Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem.
Keywords surface interpolation, multiple view stereo, specularities, shape from, level set method, variational methods, computer vision,
ISBN/ISSN/Other ISSN: 0162-8828

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