J. P. Lewis
Abstract
A solid noise is a function that defines a random value at each point in space. Solid noises have immediate and powerful applications in surface texturing, stochastic modeling, and the animation of natural phenomena.Existing solid noise synthesis algorithms are surveyed and two new algorithms are presented. The first uses Wiener interpolation to interpolate random values on a discrete lattice. The second is an efficient sparse convolution algorithm. Both algorithms are developed for model-directed synthesis, in which sampling and construction of the noise occur only at points where the noise value is required, rather than over a regularly sampled region of space. The paper attempts to present the rationale for the selection of these particular algorithms.The new algorithms have advantages of efficiency, improved control over the noise power spectrum, and the absence of artifacts. The convolution algorithm additionally allows quality to be traded for efficiency without introducing obvious deterministic effects. The algorithms are particularly suitable for applications where high-quality solid noises are required. Several sample applications in stochastic modeling and solid texturing are shown.
- 1 Abramowitz, M. and Stegun, I., Handbook of Mathematical Functions. Dover, New York, 1965.]] Google Scholar
Digital Library
- 2 Bohm, W., Farin, G. and Kahmann, J., A Survey of Curve and Surface Methods in CAGD. Computer Aided Geometric Design 1, 1 (1984), 1-60.]] Google ScholarDigital Library
- 3 Bracewell, R., The Fourier Transform and Its Applications. McGraw-Hill, New York, 1965.]]Google Scholar
- 4 Carpenter, L., Computer Rendering of Fractal Curves and Surfaces. Supplement to Proceedings of SIGGRAPH '80 (Seattle, July 1980). In Computer Graphics 14, 3 (July 1980), 180.]] Google ScholarDigital Library
- 5 Cook, R., Stochastic Sampling in Computer Graphics. ACM Transactions on Graphics 5, 1 (January 1986), 51-72.]] Google ScholarDigital Library
- 6 Cook, R., Shade Trees. Proceedings of SIGGRAPH '84 (Minneapolis, July 23-27 1984). In Computer Graphics 18, 3 (July 1984), 223-23t.]] Google ScholarDigital Library
- 7 Deutsch, R., Estimation Theory. Prentice-Hall, New Jersey, 1965.]]Google Scholar
- 8 Fournier, A., Fussell, D., and Carpenter, L., Computer Rendering of Stochastic Models. Communications ACM 25, 6 (June 1982), 371-384.]] Google ScholarDigital Library
- 9 Gardner, G., Simulation of Natural Scenes Using Textured Quadric Surfaces. Proceedings of SIGGRAPH '84 (Minneapolis, July 23-27 1984). In Computer Graphics 18, 3 (July 1984), 11-20.]] Google ScholarDigital Library
- 10 Heckbert, E, Personal communication.]]Google Scholar
- 11 Lewis, J.P., Generalized Stochastic Subdivision. ACM Transactions on Graphics 6, 3 (July 1987), 167-190.]] Google ScholarDigital Library
- 12 Lewis, J.P., Methods for Stochastic Spectral Synthesis. In Proceedings of Graphics Interface 86 (Vancouver, May 1986), 173-179.]] Google ScholarDigital Library
- 13 Oppenheim, A. and Schafer, R., Digital Signal Processing. Prentice Hall, Englewood Cliffs, N.J., 1975.]]Google Scholar
- 14 Papoulis, A., Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York, 1965.]]Google Scholar
- 15 Parke, E, Parameterized Models for Facial Animation. IEEE Computer Graphics and Applications 2, 9 (Nov. 1982), 61- 68.]]Google ScholarDigital Library
- 16 Peachy, D., Solid Texturing of Complex Surfaces. Proceedings of SIGGRAPH '85 (San Francisco, July 22-26 1985). In Computer Graphics 19, 3 (July 1985), 279-286.]] Google ScholarDigital Library
- 17 Perlin, K., A Unified Texture/Reflectance Model. In SIG- GRAPH '84 Advanced Image Synthesis course notes (Minneapolis, July 1984).]]Google Scholar
- 18 Perlin, K., An Image Synthesizer. Proceedings of SIG- GRAPH '85 (San Francisco, July 22-26 1985). In Computer Graphics 19, 3 (July 1985), 287-296.]] Google ScholarDigital Library
- 19 Schafer, R. and Rabiner, L., A Digital Signal Processing Approach to Interpolation. Proc. 1EEE 61, 6 (June 1973), 692-702.]]Google Scholar
- 20 Wiener, N., Extrapolation, interpolation, and Smoothing of Stationary Time Series. Wiley, New York, 1949.]] Google ScholarDigital Library
- 21 Yaglom, A., An Introduction to the Theory of Stationary Random Functions. Dover, New York, 1973.]]Google Scholar
Index Terms
- Algorithms for solid noise synthesis
Recommendations
Algorithms for solid noise synthesis
SIGGRAPH '89: Proceedings of the 16th annual conference on Computer graphics and interactive techniquesA solid noise is a function that defines a random value at each point in space. Solid noises have immediate and powerful applications in surface texturing, stochastic modeling, and the animation of natural phenomena.Existing solid noise synthesis ...
High Density Noise Removal by Using Cascading Algorithms
ACCT '15: Proceedings of the 2015 Fifth International Conference on Advanced Computing & Communication TechnologiesAn advanced non-linear cascading filter algorithm for the removal of high density salt and pepper noise from the digital images is proposed. The proposed method consists of two stages. The first stage Decision base Median Filter (DMF) acts as the ...
Filtering solid Gabor noise
SIGGRAPH '11: ACM SIGGRAPH 2011 papersSolid noise is a fundamental tool in computer graphics. Surprisingly, no existing noise function supports both high-quality antialiasing and continuity across sharp edges. In this paper we show that a slicing approach is required to preserve continuity ...
Comments