DeepAI AI Chat
Log In Sign Up

Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction

by   Oskar Elek, et al.

This paper investigates a novel a-posteriori variance reduction approach in Monte Carlo image synthesis. Unlike most established methods based on lateral filtering in the image space, our proposition is to produce the best possible estimate for each pixel separately, from all the samples drawn for it. To enable this, we systematically study the per-pixel sample distributions for diverse scene configurations. Noting that these are too complex to be characterized by standard statistical distributions (e.g. Gaussians), we identify patterns recurring in them and exploit those for training a variance-reduction model based on neural nets. In result, we obtain numerically better estimates compared to simple averaging of samples. This method is compatible with existing image-space denoising methods, as the improved estimates of our model can be used for further processing. We conclude by discussing how the proposed model could in future be extended for fully progressive rendering with constant memory footprint and scene-sensitive output.


page 3

page 4

page 5

page 6

page 7

page 8

page 10

page 11


Learning to Importance Sample in Primary Sample Space

Importance sampling is one of the most widely used variance reduction st...

Neural Control Variates for Variance Reduction

In statistics and machine learning, approximation of an intractable inte...

Product-form estimators: exploiting independence to scale up Monte Carlo

We introduce a class of Monte Carlo estimators for product-form target d...

Hamiltonian Flow Simulation of Rare Events

Hamiltonian Flow Monte Carlo(HFMC) methods have been implemented in engi...

Photon-Driven Neural Path Guiding

Although Monte Carlo path tracing is a simple and effective algorithm to...

Enhanced Monte Carlo Estimation of the Fisher Information Matrix with Independent Perturbations for Complex Problems

The Fisher information matrix provides a way to measure the amount of in...