Proceedings Article10.1145/311625.312153
Interactive rendering with arbitrary BRDFs using separable approximations
Jan Kautz,Michael McCool +1 more
- 01 Jul 1999
- pp 253
264
TL;DR: A separable decomposition of bidirectional reflectance distributions (BRDFs) is used to implement arbitrary reflectances from point sources on existing graphics hardware, using no more space than what is required for the final representation.
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Abstract: A separable decomposition of bidirectional reflectance distributions (BRDFs) is used to implement arbitrary reflectances from point sources on existing graphics hardware. Two-dimensional texture mapping and compositing operations are used to reconstruct samples of the BRDF at every pixel at interactive rates.
A change of variables, the Gram-Schmidt halfangle/difference vector parameterization, improves separability. Two decomposition algorithms are also presented. The singular value decomposition (SVD) minimizes RMS error. The normalized decomposition is fast and simple, using no more space than what is required for the final representation.
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Citations
Acquiring the reflectance field of a human face
Paul Debevec,Tim Hawkins,Chris Tchou,Haarm-Pieter Duiker,Westley Sarokin,Mark Sagar +5 more
- 01 Jul 2000
TL;DR: A method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint and demonstrates the technique with synthetic renderings of a person's face under novel illumination and viewpoints.
Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments
Peter-Pike Sloan,Jan Kautz,John Snyder +2 more
- 01 Jul 2002
TL;DR: A new, real-time method for rendering diffuse and glossy objects in low-frequency lighting environments that captures soft shadows, interreflections, and caustics and introduces functions for radiance transfer from a dynamic lighting environment through a preprocessed object to neighboring points in space.
A data-driven reflectance model
Wojciech Matusik,Hanspeter Pfister,Matt Brand,Leonard McMillan +3 more
- 01 Jul 2003
TL;DR: This work presents a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data that lets users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space.
A data-driven reflectance model
TL;DR: In this paper, a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data is presented, instead of using analytical reflectance models.
NeX: Real-time View Synthesis with Neural Basis Expansion
Suttisak Wizadwongsa,Pakkapon Phongthawee,Jiraphon Yenphraphai,Supasorn Suwajanakorn +3 more
- 09 Mar 2021
TL;DR: NeX as discussed by the authors models view-dependent effects by parameterizing each pixel as a linear combination of basis functions learned from a neural network and proposes a hybrid implicit-explicit modeling strategy that improves upon fine detail.
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