Adaptive polynomial modelling of the reflectance map for shape estimation from stereo and shading

Darrell R. Hougen, Narendra Ahuja

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

Abstract

This paper is concerned with estimation of the reflectance map and its use in surface shape estimation by a method that combines stereo and shading information. There are many advantages to the integrated approach. However, shape from shading algorithms are limited in their applicability by the assumption of idealized reflectance and lighting models involving many preset or hard to estimate parameters. In this paper, it is shown that the difficulties involved in estimation of the reflectance function and light source distribution can be eliminated through direct estimation of the reflectance map using adaptive polynomial models. The reflectance map estimate is used with a surface estimate provided by stereo in an integrated approach to surface shape estimation.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherPubl by IEEE
Pages991-994
Number of pages4
ISBN (Print)0818658274, 9780818658273
DOIs
StatePublished - Jan 1 1994
EventProceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Seattle, WA, USA
Duration: Jun 21 1994Jun 23 1994

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

OtherProceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CitySeattle, WA, USA
Period6/21/946/23/94

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Hougen, D. R., & Ahuja, N. (1994). Adaptive polynomial modelling of the reflectance map for shape estimation from stereo and shading. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 991-994). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). Publ by IEEE. https://doi.org/10.1109/cvpr.1994.323940