Monte Carlo Integration: Difference between revisions
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Standingpad (talk | contribs) Created page with "In the context of ray tracing, Monte Carlo Integration is used to randomly sample an image, in order to accurately compute effects such as soft shadows, glossy reflections, depth of field, motion blur, etc. efficiently.<ref>https://cs184.eecs.berkeley.edu/sp25/assets/lectures/12-integration.pdf</ref><ref>https://graphicscompendium.com/raytracing/19-monte-carlo</ref><ref>https://www.cs.cornell.edu/courses/cs4620/2013fa/lectures/22mcrt.pdf</re..." |
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In the context of [[Render Engine#Ray Tracing|ray tracing]], Monte Carlo Integration is used to randomly sample an image, in order to accurately compute effects such as soft shadows, glossy reflections, depth of field, motion blur, etc. efficiently.<ref>https://cs184.eecs.berkeley.edu/sp25/assets/lectures/12-integration.pdf</ref><ref>https://graphicscompendium.com/raytracing/19-monte-carlo</ref><ref>https://www.cs.cornell.edu/courses/cs4620/2013fa/lectures/22mcrt.pdf</ref> One side effect of Monte Carlo Integration is the addition of noise, which can be cleared up with either further sampling, or with a [[Denoiser|denoiser]]. | In the context of [[Render Engine#Ray Tracing|ray tracing]], Monte Carlo Integration is used to randomly sample an image, in order to accurately compute effects such as soft shadows, glossy reflections, depth of field, motion blur, etc. efficiently.<ref>https://cs184.eecs.berkeley.edu/sp25/assets/lectures/12-integration.pdf</ref><ref>https://graphicscompendium.com/raytracing/19-monte-carlo</ref><ref>https://www.cs.cornell.edu/courses/cs4620/2013fa/lectures/22mcrt.pdf</ref> One side effect of Monte Carlo Integration is the addition of noise, which can be cleared up with either further sampling, or with a [[Denoiser|denoiser]]. | ||
Latest revision as of 04:53, 6 July 2025
In the context of ray tracing, Monte Carlo Integration is used to randomly sample an image, in order to accurately compute effects such as soft shadows, glossy reflections, depth of field, motion blur, etc. efficiently.[1][2][3] One side effect of Monte Carlo Integration is the addition of noise, which can be cleared up with either further sampling, or with a denoiser.