Downloaded from orbit.dtu.dk on: Oct 05, 2021 Fast High-Quality Noise Frisvad, Jeppe Revall; Wyvill, Geoff Published in: Proceedings of GRAPHITE 2007 Link to article, DOI: 10.1145/1321261.1321305 Publication date: 2007 Link back to DTU Orbit Citation (APA): Frisvad, J. R., & Wyvill, G. (2007). Fast High-Quality Noise. In Proceedings of GRAPHITE 2007: 5th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia ACM. https://doi.org/10.1145/1321261.1321305 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Fast High-Quality Noise Jeppe Revall Frisvad Geoff Wyvill Technical University of Denmark University of Otago Abstract At the moment the noise functions available in a graphics program- mer’s toolbox are either slow to compute or they involve grid-line artifacts making them of lower quality. In this paper we present a real-time noise computation with no grid-line artifacts or other regularity problems. In other words, we put a new tool in the box that computes fast high-quality noise. In addition to being free of artifacts, the noise we present does not rely on tabulated data (ev- erything is computed on the fly) and it is easy to adjust quality vs. quantity for the noise. The noise is based on point rendering (like spot noise), but it extends to more than two dimensions. The fact that it is based on point rendering makes art direction of the (a) 628.1 s (b) 1.2 s noise much easier. Figure 1: Reference noise (and CPU rendering time in seconds). CR Categories: I.3.3 [Computer Graphics]: Picture/Image Gener- From left to right: (a) Pseudo-white noise filtered with a Gaussian ation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and kernel. (b) The same noise filtered with a cubic. Realism 1 Keywords: Noise, point rendering, GPU. 1 0,8 0,8 1 Introduction 0,6 0,6 We present a fast, high-quality noise computation based on raster- 0,4 ization. The noise function we implement is the sparse convolu- 0,4 tion noise advocated by J. P. Lewis [1984; 1989]. We have chosen 0,2 0,2 sparse convolution noise since it has several qualitative advantages as compared to more commonly used noise functions such as im- 0 0 proved Perlin noise. Sparse convolution noise is generally believed -0,1 -0,05 0 0,05 0,1 -0,1 -0,05 0 0,05 0,1 to be expensive to compute, but in this paper we challenge that consensus by exploiting the programmability of modern graphics Figure 2: Filter kernels. From left to right: (a) The Gaussian kernel hardware. Our approach has strong relations to van Wijk’s [1991] used in Figure 1a. It never falls off to zero. (b) The cubic kernel spot noise, but we take the concept to the next level by computing used in Figure 1b. It has compact support. solid noise using three-dimensional spots. With a few tweaks, the Perlin noise function seems to be the most This problem can also be remedied, but we have already proved our efficient way of getting individual noise values of decent quality. point; it is worth considering different approaches. At the moment It is, however, not the only way to go. By our implementation of Perlin’s very fast noise function of decent quality seems to be the sparse convolution noise for the GPU, we intend to broaden the de facto standard which everybody uses. There should be an option range of noise functions available in a graphics programmer’s tool- of at least one better quality noise function which is still fast. box. The new tool in the box is a way of computing better quality noise without a heavy efficiency penalty. 2 Reference Noise Why do we need better noise? If you happen to pick a grid-aligned 2D slice from the standard 3D implementation of improved Per- What noise functions in general try to achieve is a good approxima- lin noise [Perlin 2002], gridline artifacts will be relatively obvious, tion of white noise filtered with some Gaussian kernel [Lewis 1989; since the noise function is forced to return zero at every grid node. Ebert et al. 2002]. But as Perlin points out “this approach would ne- We might be able to fix this problem, but visually the gradient noise cessitate building a volume of white noise and then blurring it all at still seems to show a recognizable pattern, especially when grid once. This is quite impractical” [Ebert et al. 2002, p. 340]. To get a aligned. This is perhaps due to the fact that improved Perlin noise feeling for what good noise looks like, it is nevertheless interesting chooses from only a small set of different gradients at each node. to compute this reference noise. A sufficient number of pseudo- randomly placed impulses with a pseudo-random value in [−1, 1] gives a good imitation of white noise [Lewis 1989]. Suppose we are looking at a noise image with the size of 512 by 512 units (pixels) and that we use a Gaussian filter with standard deviation σ = 16/3. This gives approximately 16 Gaussian blobs across the width of the c ACM, 2007. This is the author’s version of the work. It is posted image. The resulting noise image is shown in Figure 1a. here by permission of ACM for your personal use. Not for redis- tribution. The definitive version was published in Proceedings of This Gaussian reference noise is quite expensive to compute, since GRAPHITE 2007, ACM, pp. 243–248+315, December 2007. the filter never falls off to zero, see Figure 2a. This means that every impulse contributes to every pixel. With hardly any impact on quality, we choose a cubic filter instead, see Figure 2b. Let r denote Listing 1: The fragment shader replacing an alpha texture with the filter radius (r = 16 in this case to get the same number of filter precomputed weights w(d). kernels across the image as with the Gaussian) and let d denote the uniform float filter_size_sqr; distance from the impulse to the point where we wish to compute varying vec2 winspace_vert_pos; the noise value. Then the cubic filter (which has continuous first float weight(float t) { return t*t*t; } and second derivatives) replacing the Gaussian is void main() 3 { 1 − d2/r2 , d2 < r2 w(d) = . vec2 v = gl_FragCoord.xy - winspace_vert_pos; 0 , d2 ≥ r2 float dist_sqr = dot(v, v); float w = dist < filter_size_sqr ? weight(1.0 - dist_sqr/filter_size_sqr) Now, for each noise value, we throw away all the impulses outside : 0.0; the filter radius. This greatly improves the efficiency. The result is shown in Figure 1b and is hardly distinguishable from the reference. gl_FragColor = vec4(gl_Color.rgb, w); } To have noise in different frequency ranges, we simply change the filter size r. We refer to the number of filter diameters across the width of a 2D noise image as the scale of the noise. Using this ter- minology, the noise in Figure 1 is of scale s = 16. A good quality The blending function is set such that the value measure for the sparse convolution noise is the average number n of n = 15 sources under each filter kernel. For example gives noise of vi decent quality while n = 30 gives noise of excellent quality. How- w(d) (2n1/D) ever, as n increases so does the cost of computing noise values. The two adjustable parameters s and n are convenient to have as input, is added to every pixel covered by point i. To take into account and from those we find that the number of pseudo-randomly placed that some sources are positive and some are negative, we do as fol- sources needed for a D-dimensional noise image is nsD. lows: The vertices corresponding to sources with positive values are rendered with standard additive alpha blending, while the ones 3 Point Rendering Approach with negative values are rendered with reversed subtractive alpha blending. In the reference approach described above, a filter kernel is centered at each pixel, and we need to find the distance to each source in The range of the resulting noise image is [0, 1] (this interval is ob- order to know whether it should be included in the noise calculation tained subsequently for the reference noises described in the pre- or rejected. By calculating the noise the other way around, using a vious section). The d in w(d) may be off by up to half a pixel rasterization approach, we are able to render each source as a point width because we use a texture. Otherwise we obtain the same re- and obtain exactly the same result. sult as presented in Figure 1b. Perhaps with lower precision if we do not have a high precision color buffer available, but now we have The advantage of a point rendering approach is that the graphics the result in real-time (0.0083 seconds for the image in Figure 1b) pipeline (almost for free) finds the pixels influenced by the filter even on old graphics hardware that does not support programmable kernel around a source.