Ray Tracing - Analysis of Super-Sampling Methods

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Presentation transcript:

Ray Tracing - Analysis of Super-Sampling Methods Duy & Piotr

The Problem How to reconstruct a high quality image with the least amount of samples per pixel the least amount of resources And preserving the image quality without introduction of artifacts such as jagged edges, Moire patterns, etc..

Super-sampling methods Grid Adaptive Rotated-Grid Poisson Disc Jittered Hammersley Shirley/N-Rook Monte Carlo Multi-Jittered Random (for comparison)

Multi-pixel Filtering Uses neighboring pixels to determine a better color for pixels after sampling the image. The goal is to smooth out edges without much additional computational intensity. Filters: Box Mitchell Gaussian Lanczos

2 Samples - Multi-Threaded Grid with 4 samples – 2.756

Rotated Grid with 4 samples – 2.674 seconds

Jitter with 4 samples – 2.583 seconds

Random with 2 samples – 2.631

Random with 4 samples - 3.001 seconds

NRooks with 4 samples – 2.660 seconds

Multi-Jitter with 8 pixels – 9.263

Metrics Single Thread Sample Random Grid Jittered RG Multi-Jettered Sample Random Grid Jittered RG Multi-Jettered Nrooks Box Filter 2 5.112 7.436 7.253 7.413 22.093 7.368 3 6.197 13.069 13.377 98.456 13.391 4 7.36 21.416 21.603 21.452 296.823 21.449 Guassian 4.65 7.017 7.452 7.32 24.299 7.22 5.834 14.184 14.354 14.283 115.238 14.563 Multi-Thread (no filtrer) 2.631 2.756 2.583 2.675 9.263 2.66 2.422 5.29 9.311 5.142 42.984 5.272 9 5.564