Compact Representation of Reflectance Fields using Clustered Sparse Residual Factorization

Abstract

We present a novel compression method for fixed viewpoint reflectance fields, captured for example by a Light Stage. Our compressed representation consists of a global approximation that exploits the similarities between the reflectance functions of different pixels, and a local approximation that encodes the per-pixel residual with the global approximation. Key to our method is a clustered sparse residual factorization. This sparse residual factorization ensures that the per-pixel residual matrix is as sparse as possible, enabling a compact local approximation. Finally, we demonstrate that the presented compact representation is well suited for high-quality real-time rendering.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
AD1158289

Entities

People

  • Hideshi Yamada
  • Paul Debevec
  • Pieter Peers

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Compression Ratio
  • Computations
  • Computer Graphics
  • Computer Programming
  • Computers
  • Estimators
  • Graphics
  • Image Processing
  • Linear Programming
  • Low Resolution
  • Materials
  • Mathematical Programming
  • Observation
  • Optimization
  • Pattern Recognition
  • Quadratic Programming
  • Sparse Matrix
  • Spherical Harmonics

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Computer Vision.