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Clustered principal components for precomputed radiance transfer
Peter Pike Sloan
, Jesse Hall
,
John Hart
, John Snyder
Siebel School of Computing and Data Science
Research output
:
Contribution to journal
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Conference article
›
peer-review
Overview
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Dive into the research topics of 'Clustered principal components for precomputed radiance transfer'. Together they form a unique fingerprint.
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Keyphrases
Precomputed Radiance Transfer
100%
Principal Coordinate Analysis (PCoA)
66%
Transfer Matrix
66%
Radiance
66%
Weighted Sums
33%
Surface Point
33%
Least Squares
33%
Spherical Harmonics
33%
Matrix Form
33%
Multiple Reflections
33%
Self-shadowing
33%
Graphics Hardware
33%
Subsurface Scattering
33%
Matrix Transformations
33%
Optimal Projection
33%
Few Clusters
33%
Cluster Set
33%
Spherical Harmonic Coefficients
33%
Real-time Rendering
33%
Frequency Source
33%
Representative Matrix
33%
GPU
33%
Diffuse Object
33%
Surface Signal
33%
Affine Subspace
33%
Mathematics
Matrix (Mathematics)
100%
Principal Components
100%
Spherical Harmonic
66%
Transfer Matrix
66%
Principal Component Analysis
66%
Weighted Sum
33%
Dimensional Surface
33%
Subsurface
33%
Cluster Set
33%
Representative Matrix
33%
Affine Subspace
33%
Realtime Rendering
33%
Least Squares Method
33%
Computer Science
Principal Components
100%
Component Analysis
66%
Spherical Harmonic
66%
Least Squares Method
33%
Graphic Hardware
33%
Dimensional Surface
33%
Transform Matrix
33%
Harmonic Coefficient
33%
Radiance Function
33%
Affine Subspace
33%
Graphics Processing Unit
33%