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Variational Surface Interpolation from Sparse Point and Normal Data
January 2007 (vol. 29 no. 1)
pp. 181-184
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.
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Index Terms:
Variational methods, computer vision, level set method, shape from specularities, multiple view stereo, surface interpolation.
Citation:
Jan Erik Solem, Henrik Aan?, Anders Heyden, "Variational Surface Interpolation from Sparse Point and Normal Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 181-184, Jan. 2007, doi:10.1109/TPAMI.2007.23
