Frequency domain normal map filtering

  • Authors:
  • Charles Han;Bo Sun;Ravi Ramamoorthi;Eitan Grinspun

  • Affiliations:
  • Columbia University;Columbia University;Columbia University;Columbia University

  • Venue:
  • ACM SIGGRAPH 2007 papers
  • Year:
  • 2007

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Abstract

Filtering is critical for representing detail, such as color textures or normal maps, across a variety of scales. While MIP-mapping texture maps is commonplace, accurate normal map filtering remains a challenging problem because of nonlinearities in shading---we cannot simply average nearby surface normals. In this paper, we show analytically that normal map filtering can be formalized as a spherical convolution of the normal distribution function (NDF) and the BRDF, for a large class of common BRDFs such as Lambertian, microfacet and factored measurements. This theoretical result explains many previous filtering techniques as special cases, and leads to a generalization to a broader class of measured and analytic BRDFs. Our practical algorithms leverage a significant body of work that has studied lighting-BRDF convolution. We show how spherical harmonics can be used to filter the NDF for Lambertian and low-frequency specular BRDFs, while spherical von Mises-Fisher distributions can be used for high-frequency materials.