Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain, applying pseudo-random changes to a variable, and assisting in the creation of image textures. It is most commonly implemented in two, three, or four dimensions, but can be defined for any number of dimensions.
Ken Perlin developed Perlin noise in 1983 as a result of his frustration with the "machine-like" look of computer-generated imagery (CGI) at the time. [1] He formally described his findings in a SIGGRAPH paper in 1985 called "An Image Synthesizer". [2] He developed it after working on Disney's computer animated sci-fi motion picture Tron (1982) for the animation company Mathematical Applications Group (MAGI). [3] In 1997, Perlin was awarded an Academy Award for Technical Achievement for creating the algorithm, the citation for which read: [4] [5] [6] [7]
To Ken Perlin for the development of Perlin Noise, a technique used to produce natural appearing textures on computer generated surfaces for motion picture visual effects. The development of Perlin Noise has allowed computer graphics artists to better represent the complexity of natural phenomena in visual effects for the motion picture industry.
Perlin did not apply for any patents on the algorithm, but in 2001 he was granted a patent for the use of 3D+ implementations of simplex noise for texture synthesis. Simplex noise has the same purpose, but uses a simpler space-filling grid. Simplex noise alleviates some of the problems with Perlin's "classic noise", among them computational complexity and visually-significant directional artifacts. [8]
Perlin noise is a procedural texture primitive, a type of gradient noise used by visual effects artists to increase the appearance of realism in computer graphics. The function has a pseudo-random appearance, yet all of its visual details are the same size. This property allows it to be readily controllable; multiple scaled copies of Perlin noise can be inserted into mathematical expressions to create a great variety of procedural textures. Synthetic textures using Perlin noise are often used in CGI to make computer-generated visual elements –such as object surfaces, fire, smoke, or clouds –appear more natural, by imitating the controlled random appearance of textures in nature.
It is also frequently used to generate textures when memory is extremely limited, such as in demos. Its successors, such as fractal noise and simplex noise, have become nearly ubiquitous in graphics processing units both for real-time graphics and for non-real-time procedural textures in all kinds of computer graphics.
It is frequently used in video games to make procedurally generated terrain that looks natural. This success is in part due to the hierarchical structuring of Perlin noise that mimics naturally occurring hierarchical structures, and therefore also has found to be useful in environmental science applications. [9]
Perlin noise is most commonly implemented as a two-, three- or four-dimensional function, but can be defined for any number of dimensions. An implementation typically involves three steps: defining a grid of random gradient vectors, computing the dot product between the gradient vectors and their offsets, and interpolation between these values. [7]
Define an n-dimensional grid where each grid intersection has associated with it a fixed random n-dimensional unit-length gradient vector, except in the one dimensional case where the gradients are random scalars between −1 and 1.
For working out the value of any candidate point, first find the unique grid cell in which the point lies. Then, identify the 2n corners of that cell and their associated gradient vectors. Next, for each corner, calculate an offset vector. An offset vector is a displacement vector from that corner to the candidate point.
For each corner, we take the dot product between its gradient vector and the offset vector to the candidate point. This dot product will be zero if the candidate point is exactly at the grid corner.
Note that a gradient vector's influence grows with distance, which can be avoided by normalizing the offset vector to a length of 1 first. This would introduce noticeable sharp changes, except the distance is taken into account in the following interpolation step. Normalizing the offset vector is however not a common practice.
For a point in a two-dimensional grid, this will require the computation of four offset vectors and dot products, while in three dimensions it will require eight offset vectors and eight dot products. In general, the algorithm has O(2n) complexity in n dimensions.
The final step is interpolation between the 2n dot products. Interpolation is performed using a function that has zero first derivative (and possibly also second derivative) at the 2n grid nodes. Therefore, at points close to the grid nodes, the output will approximate the dot product of the gradient vector of the node and the offset vector to the node. This means that the noise function will pass through 0 at every node, giving Perlin noise its characteristic look.
If n = 1, an example of a function that interpolates between value a0 at grid node 0 and value a1 at grid node 1 is
where the smoothstep function was used.
Noise functions for use in computer graphics typically produce values in the range [–1.0, 1.0] and can be scaled accordingly.
Many implementations of Perlin noise use the same permutation set that Ken Perlin used in his original implementation. [10] That implementation is as follows:
intpermutation[]={151,160,137,91,90,15,131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,190,6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,88,237,149,56,87,174,20,125,136,171,168,68,175,74,165,71,134,139,48,27,166,77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,102,143,54,65,25,63,161,1,216,80,73,209,76,132,187,208,89,18,169,200,196,135,130,116,188,159,86,164,100,109,198,173,186,3,64,52,217,226,250,124,123,5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,223,183,170,213,119,248,152,2,44,154,163,70,221,153,101,155,167,43,172,9,129,22,39,253,19,98,108,110,79,113,224,232,178,185,112,104,218,246,97,228,251,34,242,193,238,210,144,12,191,179,162,241,81,51,145,235,249,14,239,107,49,192,214,31,181,199,106,157,184,84,204,176,115,121,50,45,127,4,150,254,138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180};This specific permutation is not absolutely required, though it does require a randomized array of the integers 0 to 255. If creating a new permutation table, care should be taken to ensure uniform distribution of the values. [11]
For each evaluation of the noise function, the dot product of the position and gradient vectors must be evaluated at each node of the containing grid cell. Perlin noise therefore scales with complexity O(2n) for n dimensions. Alternatives to Perlin noise producing similar results with improved complexity scaling include simplex noise and OpenSimplex noise.
A fractal landscape or fractal surface is generated using a stochastic algorithm designed to produce fractal behavior that mimics the appearance of natural terrain. In other words, the surface resulting from the procedure is not a deterministic, but rather a random surface that exhibits fractal behavior.
Kenneth H. Perlin is a professor in the Department of Computer Science at New York University, founding director of the Media Research Lab at NYU, director of the Future Reality Lab at NYU, and the Director of the Games for Learning Institute. He holds a BA. degree in Theoretical Mathematics from Harvard University (7/1979), a MS degree in Computer Science from the Courant Institute of Mathematical Sciences, New York University (6/1984), and a PhD degree in Computer Science from the same institution (2/1986). His research interests include graphics, animation, multimedia, and science education. He developed or was involved with the development of techniques such as Perlin noise, real-time interactive character animation, and computer-user interfaces. He is best known for the development of Perlin noise and Simplex noise, both of which are algorithms for realistic-looking Gradient noise.
In computing, procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated content and algorithms coupled with computer-generated randomness and processing power. In computer graphics, it is commonly used to create textures and 3D models. In video games, it is used to automatically create large amounts of content in a game. Depending on the implementation, advantages of procedural generation can include smaller file sizes, larger amounts of content, and randomness for less predictable gameplay.
Texture synthesis is the process of algorithmically constructing a large digital image from a small digital sample image by taking advantage of its structural content. It is an object of research in computer graphics and is used in many fields, amongst others digital image editing, 3D computer graphics and post-production of films.
In electrical engineering and computer science, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm named after Stuart P. Lloyd for finding evenly spaced sets of points in subsets of Euclidean spaces and partitions of these subsets into well-shaped and uniformly sized convex cells. Like the closely related k-means clustering algorithm, it repeatedly finds the centroid of each set in the partition and then re-partitions the input according to which of these centroids is closest. In this setting, the mean operation is an integral over a region of space, and the nearest centroid operation results in Voronoi diagrams.
The plasma effect is a computer-based visual effect animated in real-time. It uses cycles of changing colours warped in various ways to give an illusion of liquid, organic movement.
Simplex noise is the result of an n-dimensional noise function comparable to Perlin noise but with fewer directional artifacts, in higher dimensions, and a lower computational overhead. Ken Perlin designed the algorithm in 2001 to address the limitations of his classic noise function, especially in higher dimensions.
Filter Forge is a computer graphics program for Windows and Mac that allows users to create procedural textures and modify images. It can be used as a standalone application or as a plugin for compatible 8bf hosts such as Adobe Photoshop, Affinity Photo, Corel PaintShop Pro. It has been under continuous development by Filter Forge OÜ since its official release in March 2007.
Simulation noise is a function that creates a divergence-free vector field. This signal can be used in artistic simulations for the purposes of increasing the perception of extra detail.
In numerical analysis, multivariate interpolation is interpolation on functions of more than one variable ; when the variates are spatial coordinates, it is also known as spatial interpolation.
A scenery generator is software used to create landscape images, 3D models, and animations. These programs often use procedural generation to generate the landscapes. If not using procedural generation to create the landscapes, then normally a 3D artist would render and create the landscapes. These programs are often used in video games or movies. Basic elements of landscapes created by scenery generators include terrain, water, foliage, and clouds. The process for basic random generation uses a diamond square algorithm.
Value noise is a type of noise commonly used as a procedural texture primitive in computer graphics. It is conceptually different from, and often confused with gradient noise, examples of which are Perlin noise and Simplex noise. This method consists of the creation of a lattice of points which are assigned random values. The noise function then returns the interpolated number based on the values of the surrounding lattice points.
Gradient noise is a type of noise commonly used as a procedural texture primitive in computer graphics. It is conceptually different from, and often confused with, value noise. This method consists of a creation of a lattice of random gradients, dot products of which are then interpolated to obtain values in between the lattices. An artifact of some implementations of this noise is that the returned value at the lattice points is 0. Unlike the value noise, gradient noise has more energy in the high frequencies.
Worley noise, also called Voronoi noise and cellular noise, is a noise function introduced by Steven Worley in 1996. Worley noise is an extension of the Voronoi diagram that outputs a real value at a given coordinate that corresponds to the Distance of the nth nearest seed and the seeds are distributed evenly through the region. Worley noise is used to create procedural textures.
In computer graphics, a procedural texture is a texture created using a mathematical description rather than directly stored data. The advantage of this approach is low storage cost, unlimited texture resolution and easy texture mapping. These kinds of textures are often used to model surface or volumetric representations of natural elements such as wood, marble, granite, metal, stone, and others.
OpenSimplex noise is an n-dimensional gradient noise function that was developed in order to overcome the patent-related issues surrounding simplex noise, while likewise avoiding the visually-significant directional artifacts characteristic of Perlin noise.
This is a glossary of terms relating to computer graphics.
Noise is any type of random, troublesome, problematic, or unwanted signals.
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