Journal article
Variational Surface Interpolation from Sparse Point and Normal Data
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.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2007 |
| Pages: | 181-184 |
| ISSN: | 19393539 , 01628828 and 21609292 |
| Types: | Journal article |
| DOI: | 10.1109/TPAMI.2007.250610 |
| ORCIDs: | Aanæs, Henrik |
Algorithms Artifacts Artificial Intelligence Cameras Computer vision Image Enhancement Image Interpretation, Computer-Assisted Image reconstruction Imaging, Three-Dimensional Information Storage and Retrieval Interpolation Level set Measurement standards Pattern Recognition, Automated Shape Signal Processing, Computer-Assisted Stereo vision Surface reconstruction Surface texture Variational methods computer vision image reconstruction interpolation level set method multiple view stereo shape from specularities sparse variational constraint surface interpolation. surface reconstruction variational surface interpolation variational techniques
