A Sparse Parametric Mixture Model for BTF Compression, Editing and Rendering

نویسندگان

  • Hongzhi Wu
  • Julie Dorsey
  • Holly E. Rushmeier
چکیده

Bidirectional texture functions (BTFs) represent the appearance of complex materials. Three major shortcomings with BTFs are the bulky storage, the difficulty in editing and the lack of efficient rendering methods. To reduce storage, many compression techniques have been applied to BTFs, but the results are difficult to edit. To facilitate editing, analytical models have been fit, but at the cost of accuracy of representation for many materials. It becomes even more challenging if efficient rendering is also needed. We introduce a high-quality general representation that is, at once, compact, easily editable, and can be efficiently rendered. The representation is computed by adopting the stagewise Lasso algorithm to search for a sparse set of analytical functions, whose weighted sum approximates the input appearance data. We achieve compression rates comparable to a state-of-the-art BTF compression method. We also demonstrate results in BTF editing and rendering.

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عنوان ژورنال:
  • Comput. Graph. Forum

دوره 30  شماره 

صفحات  -

تاریخ انتشار 2011