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You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Blender supports Python scripting for the creation of custom tools, prototyping, importing/exporting from other formats, and task automation. This allows for integration with several external render engines through plugins/addons. Blender itself can also be compiled & imported as a python library for further automation and development.
You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Essentially, the rendering process tries to depict a continuous function from image space to colors by using a finite number of pixels. As a consequence of the Nyquist–Shannon sampling theorem (or Kotelnikov theorem), any spatial waveform that can be displayed must consist of at least two pixels, which is proportional to image resolution .
Plane model segmentation - simple algorithm that finds all the points that support a plane model in the point cloud Euclidean clustering - creates clusters of points based on Euclidean distance Conditional Euclidean clustering - clustering points based on Euclidean distance and a user-defined condition
The number-theoretic transform (NTT) [4] is obtained by specializing the discrete Fourier transform to = /, the integers modulo a prime p. This is a finite field , and primitive n th roots of unity exist whenever n divides p − 1 {\displaystyle p-1} , so we have p = ξ n + 1 {\displaystyle p=\xi n+1} for a positive integer ξ .
A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1. [1]