A Radiative Transfer Framework for Spatially-Correlated Materials
May 07, 2018 Β· Declared Dead Β· π ACM Transactions on Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Adrian Jarabo, Carlos Aliaga, Diego Gutierrez
arXiv ID
1805.02651
Category
cs.GR: Graphics
Citations
70
Venue
ACM Transactions on Graphics
Last Checked
3 months ago
Abstract
We introduce a non-exponential radiative framework that takes into account the local spatial correlation of scattering particles in a medium. Most previous works in graphics have ignored this, assuming uncorrelated media with a uniform, random local distribution of particles. However, positive and negative correlation lead to slower- and faster-than-exponential attenuation respectively, which cannot be predicted by the Beer-Lambert law. As our results show, this has a major effect on extinction, and thus appearance. From recent advances in neutron transport, we first introduce our Extended Generalized Boltzmann Equation, and develop a general framework for light transport in correlated media. We lift the limitations of the original formulation, including an analysis of the boundary conditions, and present a model suitable for computer graphics, based on optical properties of the media and statistical distributions of scatterers. In addition, we present an analytic expression for transmittance in the case of positive correlation, and show how to incorporate it efficiently into a Monte Carlo renderer. We show results with a wide range of both positive and negative correlation, and demonstrate the differences compared to classic light transport.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Graphics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Everybody Dance Now
R.I.P.
π»
Ghosted
Deep Bilateral Learning for Real-Time Image Enhancement
R.I.P.
π»
Ghosted
Animating Human Athletics
R.I.P.
π»
Ghosted
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
R.I.P.
π»
Ghosted
Shape Transformation Using Variational Implicit Functions
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted