In this paper, an effective system for synthesizing animal skin patterns on arbitrary polygonal surfaces is developed. To accomplish the task, a system inspired by the Clonal Mosaic （CM） model is proposed. The CM mo...In this paper, an effective system for synthesizing animal skin patterns on arbitrary polygonal surfaces is developed. To accomplish the task, a system inspired by the Clonal Mosaic （CM） model is proposed. The CM model simulates cells＇ reactions on arbitrary surface. By controlling the division, mutation and repulsion of cells, a regulated spatial arrangement of cells is formed. This arrangement of cells shows appealing result, which is comparable with those natural patterns observed from animal skin. However, a typical CM simulation process incurs high computational cost, where the distances among ceils across a polygonal surface are measured and the movements of cells are constrained on the surface. In this framework, an approach is proposed to transform each of the original 3D geometrical planes of the surface into its Canonical Reference Plane Structure. This structure helps to simplify a 3D computational problem into a more manageable 2D problem. Furthermore, the concept of Local Relaxation is developed to optimally enhance the relaxation process for a typical CM simulation. The performances of the proposed solution methods have been verified with extensive experimental results.展开更多
Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and rando...Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and randomly accessible.Traditional lattice noise functions such as Perlin noise,however,exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients.In this paper,we introduce a parameterized lattice noise called prime gradient noise(PGN)that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency.PGN utilizes prime gradients,a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system.To map axial indices of lattice vertices to prime gradients,PGN employs Szudzik pairing,a bijection F:N2→N.Compositions of Szudzik pairing functions are used in higher dimensions.At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals.A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence,periodicity in the noise texture.We demonstrate the utility of the proposed noise function with several examples in procedural modeling,parameterized pattern synthesis,and solid texturing.展开更多
文摘In this paper, an effective system for synthesizing animal skin patterns on arbitrary polygonal surfaces is developed. To accomplish the task, a system inspired by the Clonal Mosaic （CM） model is proposed. The CM model simulates cells＇ reactions on arbitrary surface. By controlling the division, mutation and repulsion of cells, a regulated spatial arrangement of cells is formed. This arrangement of cells shows appealing result, which is comparable with those natural patterns observed from animal skin. However, a typical CM simulation process incurs high computational cost, where the distances among ceils across a polygonal surface are measured and the movements of cells are constrained on the surface. In this framework, an approach is proposed to transform each of the original 3D geometrical planes of the surface into its Canonical Reference Plane Structure. This structure helps to simplify a 3D computational problem into a more manageable 2D problem. Furthermore, the concept of Local Relaxation is developed to optimally enhance the relaxation process for a typical CM simulation. The performances of the proposed solution methods have been verified with extensive experimental results.
基金supported by the National Science and Engineering Research Council of Canada(NSERC)Discovery Grant No.2019-05092。
文摘Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and randomly accessible.Traditional lattice noise functions such as Perlin noise,however,exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients.In this paper,we introduce a parameterized lattice noise called prime gradient noise(PGN)that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency.PGN utilizes prime gradients,a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system.To map axial indices of lattice vertices to prime gradients,PGN employs Szudzik pairing,a bijection F:N2→N.Compositions of Szudzik pairing functions are used in higher dimensions.At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals.A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence,periodicity in the noise texture.We demonstrate the utility of the proposed noise function with several examples in procedural modeling,parameterized pattern synthesis,and solid texturing.
