[RAY TRACING] 5 Stochastic Sampling and Distributed Ray Tracing

Stochastic Sampling and Distributed Ray Tracing

1. Introduction

Rendering algorithms can be classified as analytic and discrete according to how they approach the aliasing problem. Discrete is better than analytic algorithms.

  • super sampling
  • adaptive sampling
  • stochastic sampling (here we introduce)

2. Uniform point sampling

A review to uniform sampling as mentioned in paragraph 1.

3.Poisson disk sampling

Nonuniform distribution sample found in human eye called Poisson disk distribution, which replaces aliasing by noise.

4. Jittering a regular gird

This method produces good results and is well suited to image rendering algorithms, but it can remain small amount of aliasing as it is not as good as Poisson disk sampling.

5.Distributed ray tracing

Definition of distributed (probabilistic) ray tracing

If we regard the variables of the integration as additional dimensions, we can perform a Monte Carlo evaluation of the integrals by stochastically distributing the sample points(rays) in those additional dimensions.

5.1 Shading

How to calculate intensity I of reflected light at a point on the surface.

5.2 Depth of field

I don’t understand the formulas thoroughly.

 

 

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