The paper "Stochastic Sampling in Computer Graphics" by Robert L. Cook from Pixar addresses the issue of aliasing artifacts in ray tracing and ray casting techniques. Aliasing, which occurs when high frequencies are aliased as low frequencies, is a common problem in computer graphics due to the discrete nature of pixel sampling. The author proposes a solution called stochastic sampling, which involves using nonuniformly spaced samples to avoid aliasing. This method replaces aliasing with noise of the correct average intensity, which is less objectionable to the human visual system. Stochastic sampling is demonstrated through the use of Poisson disk sampling, a distribution inspired by the human eye's photoreceptor arrangement. This distribution ensures that samples are not too closely packed, reducing noise. Jittering a regular grid is also discussed as an alternative method to achieve a similar effect. The paper further explores the application of stochastic sampling in distributed ray tracing, where rays are stochastically distributed to simulate phenomena such as motion blur, depth of field, penumbras, gloss, and translucency. The benefits of stochastic sampling include improved image quality, reduced aliasing, and the ability to simulate complex effects. However, it also introduces additional computational overhead. The paper concludes with examples and discussions on the effectiveness of stochastic sampling in various scenarios, emphasizing its potential to enhance the realism and visual appeal of computer-generated images.The paper "Stochastic Sampling in Computer Graphics" by Robert L. Cook from Pixar addresses the issue of aliasing artifacts in ray tracing and ray casting techniques. Aliasing, which occurs when high frequencies are aliased as low frequencies, is a common problem in computer graphics due to the discrete nature of pixel sampling. The author proposes a solution called stochastic sampling, which involves using nonuniformly spaced samples to avoid aliasing. This method replaces aliasing with noise of the correct average intensity, which is less objectionable to the human visual system. Stochastic sampling is demonstrated through the use of Poisson disk sampling, a distribution inspired by the human eye's photoreceptor arrangement. This distribution ensures that samples are not too closely packed, reducing noise. Jittering a regular grid is also discussed as an alternative method to achieve a similar effect. The paper further explores the application of stochastic sampling in distributed ray tracing, where rays are stochastically distributed to simulate phenomena such as motion blur, depth of field, penumbras, gloss, and translucency. The benefits of stochastic sampling include improved image quality, reduced aliasing, and the ability to simulate complex effects. However, it also introduces additional computational overhead. The paper concludes with examples and discussions on the effectiveness of stochastic sampling in various scenarios, emphasizing its potential to enhance the realism and visual appeal of computer-generated images.