|
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.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
Abramowitz, M. and Stegun, I., Handbook of Mathematical Functions. Dover, New York, 1965.
|
| |
2
|
|
| |
3
|
Bracewell, R., The Fourier Transform and Its Applications. McGraw-Hill, New York, 1965.
|
 |
4
|
|
 |
5
|
|
 |
6
|
|
| |
7
|
Deutsch, R., Estimation Theory. Prentice-Hall, New Jersey, 1965.
|
 |
8
|
|
 |
9
|
|
| |
10
|
Heckbert, E, Personal communication.
|
 |
11
|
|
| |
12
|
|
| |
13
|
Oppenheim, A. and Schafer, R., Digital Signal Processing. Prentice Hall, Englewood Cliffs, N.J., 1975.
|
| |
14
|
Papoulis, A., Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York, 1965.
|
| |
15
|
Parke, E, Parameterized Models for Facial Animation. IEEE Computer Graphics and Applications 2, 9 (Nov. 1982), 61- 68.
|
 |
16
|
|
| |
17
|
Perlin, K., A Unified Texture/Reflectance Model. In SIG- GRAPH '84 Advanced Image Synthesis course notes (Minneapolis, July 1984).
|
 |
18
|
|
| |
19
|
Schafer, R. and Rabiner, L., A Digital Signal Processing Approach to Interpolation. Proc. 1EEE 61, 6 (June 1973), 692-702.
|
| |
20
|
|
| |
21
|
Yaglom, A., An Introduction to the Theory of Stationary Random Functions. Dover, New York, 1973.
|
CITED BY 33
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Zhengyou Zhang , Zicheng Liu , Dennis Adler , Michael F. Cohen , Erik Hanson , Ying Shan, Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach, International Journal of Computer Vision, v.58 n.2, p.93-119, July 2004
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
G. Székely , Ch. Brechbühler , J. Dual , R. Enzler , J. Hug , R. Hutter , N. Ironmonger , M. Kauer , V. Meier , P. Niederer , A. Rhomberg , P. Schmid , G. Schweitzer , M. Thaler , V. Vuskovic , G. Tröster , U. Haller , M. Bajka, Virtual Reality-Based Simulation of Endoscopic Surgery, Presence: Teleoperators and Virtual Environments, v.9 n.3, p.310-333, June 2000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Ares Lagae , Craig S. Kaplan , Chi-Wing Fu , Victor Ostromoukhov , Oliver Deussen, Tile-based methods for interactive applications, ACM SIGGRAPH 2008 classes, August 11-15, 2008, Los Angeles, California
|
|
|
|
|
|
|
|
|
|
|