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Variational surface interpolation from sparse point and normal data

In English

Författare

Summary, in 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.

Avdelning/ar

Publiceringsår

2007

Språk

Engelska

Sidor

181-184

Publikation/Tidskrift/Serie

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volym

29

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Mathematics

Nyckelord

  • surface interpolation
  • multiple view stereo
  • specularities
  • shape from
  • level set method
  • variational methods
  • computer vision

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1939-3539