Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks.
Virtual Ray Lights for Rendering Scenes with Participating Media
We present an efficient many-light algorithm for simulating indirect illumination in, and from, participating media. Instead of creating discrete virtual point lights (VPLs) at vertices of random-walk paths, we present a continuous generalization that places virtual ray lights (VRLs) along each path segment in the medium.
Appearance Capture and Modeling of Human Teeth
We present a system specifically designed for capturing the optical properties of live human teeth such that they can be realistically re-rendered in computer graphics.
Nonlinearly Weighted First-Order Regression for Denoising Monte Carlo Renderings
We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide range of scenes.
Progressive Virtual Beam Lights
We present Virtual Beam Lights (VBLs), a progressive many-lights algorithm for rendering complex indirect transport paths in, from, and to media.
Residual Ratio Tracking for Estimating Attenuation in Participating Media
We present ratio tracking and residual tracking, two complementary techniques that can be combined into an efficient, unbiased estimator for evaluating transmittance in complex heterogeneous media.
Portal-Masked Environment Map Sampling
We present a technique to efficiently importance sample distant, all-frequency illumination in indoor scenes.
Subdivision Next-Event Estimation for Path-Traced Subsurface Scattering
We present subdivision next-event estimation (SNEE) for unbiased Monte Carlo simulation of subsurface scattering.
Reversible Jump Metropolis Light Transport using Inverse Mappings
We investigate how to generalize this concept to non-invertible sampling techniques commonly found in practice, and introduce probabilistic inverses that extend our perturbation to cover most sampling methods found in light transport simulations.
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