1 / 8

Parallelized Monte Carlo Raytracing

Parallelized Monte Carlo Raytracing. Brian Anderson. Simple Raytracing. Shoot a single ray from the eye. Determine closest intersection point. Sum the intensity of lights visible from the point. Recursively shoot single reflection and refraction rays as necessary. Assumes:

matilde
Télécharger la présentation

Parallelized Monte Carlo Raytracing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ParallelizedMonte Carlo Raytracing Brian Anderson

  2. Simple Raytracing • Shoot a single ray from the eye. • Determine closest intersection point. • Sum the intensity of lights visible from the point. • Recursively shoot single reflection and refraction rays as necessary. • Assumes: • Point Lights (either visible or not) • Perfect Reflections • Camera with infinitely small aperature

  3. Monte Carlo Raytracing • Shoot (many) random rays from the aperature. • Determine intersection point. • Sum visible and partially visible light sources. • Shoot (many) random rays to the area of each light. • For fuzzy reflections: • Emit reflection rays in(many) random directions. • Weight appropriately. • = LOTS and LOTS of rays!

  4. Parallelizing Monte Carlo • Have each proccess calculate a single area of the image? • Have each process calculate it’s own image and then average all of them? • Not completely straightforward…

  5. Convergence of Monte Carlo • Quality does not only depend on number of rays. • Key: Minimize variance  Decrease noise. • Using completely random rays: • Variance converges with √N. • We can do better by selectively choosing a good distribution of rays: • Importance Sampling • Stratisfied Sampling • Variance can converge with N.

  6. Parallelizing Monte Carlo • Useful for: • Fuzzy Reflections • Easily parallelized: • Probabalistic • No coordination needed • Useful for: • Depth of Field • Soft Shadows • Parallelize by: • Partitioning strata • 1 disc with 16 strata >4 discs with 4 strata • Importance Sampling: • Stratisfication:  X

  7. Results • If parallelized naiively, serial version could converge faster, negating added processing power. • Parallelized code does not yet work. • Using methods indicated, suspect N times speedup for identical quality.

More Related