NDLI: TensorTextures: multilinear image-based rendering

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TensorTextures: multilinear image-based rendering

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TensorTextures: multilinear image-based rendering

Content Provider	ACM Digital Library
Author	Vasilescu, M. Alex O. Terzopoulos, Demetri
Abstract	This paper introduces a tensor framework for image-based rendering. In particular, we develop an algorithm called TensorTextures that learns a parsimonious model of the bidirectional texture function (BTF) from observational data. Given an ensemble of images of a textured surface, our nonlinear, generative model explicitly represents the multifactor interaction implicit in the detailed appearance of the surface under varying photometric angles, including local (per-texel) reflectance, complex mesostructural self-occlusion, interreflection and self-shadowing, and other BTF-relevant phenomena. Mathematically, TensorTextures is based on multilinear algebra, the algebra of higher-order tensors, hence its name. It is computed through a decomposition known as the N-mode SVD, an extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the application of TensorTextures to the image-based rendering of natural and synthetic textured surfaces under continuously varying viewpoint and illumination conditions.
Starting Page	336
Ending Page	342
Page Count	7
File Format	PDF
DOI	10.1145/1186562.1015725
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Tensor decomposition Tensors Textured surface rendering Statistical learning Multilinear algebra Bidirectional texture function Image-based rendering
Content Type	Text
Resource Type	Article

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