Noise-resistant fitting for spherical harmonics

Ping-Man Lam, Chi-Sing Leung, Tien-Tsin Wong

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)

Abstract

Spherical harmonic (SH) basis functions have been widely used for representing spherical functions in modeling various illumination properties. They can compactly represent low-frequency spherical functions. However, when the unconstrained least Square method is used for estimating the SH coefficients of a hemispherical function, the magnitude of these SH coefficients could De very large. Hence, the rendering result is very sensitive to quantization noise (introduced by modern texture compression like S3TC, IEEE half float data type on GPU, or other lossy compression methods) in these SH coefficients. Our experiments show that, as the precision of SH coefficients is reduced, the rendered images may exhibit annoying visual art if acts. To reduce the noise sensitivity of the SH coefficients, this paper first discusses how the magnitude of SH coefficients affects the rendering result when there is Quantization noise. Then, two fast fitting methods for estimating the noise resistant SH coefficients are proposed. They can effectively control the magnitude of the estimated SH coefficients and, hence, suppress the rendering artifacts. Both statistical and visual results confirm our theory.

Original languageEnglish
Pages (from-to)254-265
Number of pages12
JournalIEEE Transactions on Visualization and Computer Graphics
Volume12
Issue number2
DOIs
Publication statusPublished - Mar 2006
Externally publishedYes

Keywords

  • BRDF
  • Constrained least Square
  • Image-based relighting
  • Noise resistant fitting
  • Precomputed radiance transfer
  • Spherical harmonics
  • Texture compression

Cite this