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[BKR+05]  Line Drawings from Volume Data

Burns:2005:LDV (Article)
Author(s)Burns M., Klawe J., Rusinkiewicz S., Finkelstein A. and DeCarlo D.
Title« Line Drawings from Volume Data »
JournalACM Transactions on Graphics, Proceedings of ACM SIGGRAPH 2005 (Los Angeles, CA, July 31--August 4, 2005)
Volume24
Number3
Page(s)512--518
Year2005

Abstract
Renderings of volumetric data have become an important data analysis tool for applications ranging from medicine to scientific simulation. We propose a volumetric drawing system that directly extracts sparse linear features, such as silhouettes and suggestive contours, using a temporally coherent seed-and-traverse framework. In contrast to previous methods based on isosurfaces or nonrefractive transparency, producing these drawings requires examining an asymptotically smaller subset of the data, leading to efficiency on large data sets. In addition, the resulting imagery is often more comprehensible than standard rendering styles, since it focuses attention on important features in the data. We test our algorithms on datasets up to 5123, demonstrating interactive extraction and rendering of line drawings in a variety of drawing styles.

BibTeX code
@article{Burns:2005:LDV,
  optpostscript = {},
  number = {3},
  month = jul,
  author = {Michael Burns and Janek Klawe and Szymon Rusinkiewicz and Adam
            Finkelstein and Doug DeCarlo},
  optkey = {},
  optannote = {},
  localfile = {papers/Burns.2005.LDV.pdf},
  optkeywords = {},
  doi = {http://doi.acm.org/10.1145/1073204.1073222},
  optciteseer = {},
  journal = SIGGRAPH2005,
  opturl = {},
  volume = {24},
  optwww = {},
  title = {{L}ine {D}rawings from {V}olume {D}ata},
  abstract = {Renderings of volumetric data have become an important data
              analysis tool for applications ranging from medicine to scientific
              simulation. We propose a volumetric drawing system that directly
              extracts sparse linear features, such as silhouettes and
              suggestive contours, using a temporally coherent seed-and-traverse
              framework. In contrast to previous methods based on isosurfaces or
              nonrefractive transparency, producing these drawings requires
              examining an asymptotically smaller subset of the data, leading to
              efficiency on large data sets. In addition, the resulting imagery
              is often more comprehensible than standard rendering styles, since
              it focuses attention on important features in the data. We test
              our algorithms on datasets up to 5123, demonstrating interactive
              extraction and rendering of line drawings in a variety of drawing
              styles.},
  pages = {512--518},
  year = {2005},
}

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