On optimal resampling of view and illumination dependent textures

Jir̂í Filip, Michael J. Chantler, Michal Haind

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The use of illumination and view dependent textural information is one way to capture the realistic appearance of genuine materi- als. One example of such data is the bidirectional texture function. The main disadvantage of these data, that makes their further ap- plication very difficult, is their massive size. Perceptually-based methods can determine optimal uniform resampling of these data that allows considerable reduction of a number of view and illumi- nation dependent samples. In this paper we propose to achieve this goal by means of a psychophysical study, comparing original data rendering with rendering of their uniformly resampled version over the hemisphere of illumination and view dependent textural mea- surements. The resampling was done separately for elevation and azimuthal angles as well as in illumination and view space. Our results shown promising consequences for compression and mod- eling algorithms using this kind of massive data © 2008 ACM.

Original languageEnglish
Title of host publicationAPGV 2008 - Proceedings of the Symposium on Applied Perception in Graphics and Visualization
Pages131-134
Number of pages4
DOIs
Publication statusPublished - 2008
EventSymposium on Applied Perception in Graphics and Visualization, APGV 2008 - Los Angeles, CA, United States
Duration: 9 Aug 200810 Aug 2008

Conference

ConferenceSymposium on Applied Perception in Graphics and Visualization, APGV 2008
CountryUnited States
CityLos Angeles, CA
Period9/08/0810/08/08

Keywords

  • BTF
  • optimal resampling
  • Phy-chophysical experiment
  • Texture compression
  • visual degradation

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    Filip, J., Chantler, M. J., & Haind, M. (2008). On optimal resampling of view and illumination dependent textures. In APGV 2008 - Proceedings of the Symposium on Applied Perception in Graphics and Visualization (pp. 131-134) https://doi.org/10.1145/1394281.1394305