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Parallelized Monte Carlo Raytracing

This project explores Parallelized Monte Carlo Raytracing techniques to render high-quality images with realistic lighting effects. By shooting rays from the camera's eye to determine intersection points and calculating light intensities visible from those points, the algorithm simulates reflection and refraction. It accounts for point lights, perfect reflections, and a camera with an infinitesimally small aperture. The Monte Carlo approach involves shooting random rays from the aperture, summing visible light sources, and bouncing rays for fuzzy reflections. The convergence of Monte Carlo is discussed, emphasizing the importance of minimizing variance to reduce noise. Through selective ray distribution using Importance Sampling and Stratified Sampling, variance can converge faster with fewer rays. Parallelizing Monte Carlo is shown to benefit applications like fuzzy reflections, depth of field, and soft shadows, with strategies for partitioning strata to achieve parallel processing efficiency. The potential challenges of parallelizing Monte Carlo and the importance of strategic implementation strategies are highlighted to maximize performance gains while maintaining image quality.

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Parallelized Monte Carlo Raytracing

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  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.

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