Kaiserslautern - Fachbereich Informatik
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We introduce two novel techniques for speeding up the generation of digital \((t,s)\)-sequences. Based on these results a new algorithm for the construction of Owen's randomly permuted \((t,s)\)-sequences is developed and analyzed. An implementation of the new techniques is available at http://www.cs.caltech.edu/~ilja/libseq/index.html
Interleaved Sampling
(2001)
The sampling of functions is one of the most fundamental tasks in computer graphics, and occurs in a variety of different forms. The known sampling methods can roughly be grouped in two categories. Sampling on regular grids is simple and efficient, and the algorithms are often easy to built into graphics hardware. On the down side, regular sampling is prone to aliasing artifacts that are expensive to overcome. Monte Carlo methods, on the other hand,
mask the aliasing artifacts by noise. However due to the lack of coherence, these methods are more expensive and not weil suited for hardware implementations. In this paper, we introduce a novel sampling scheme where samples from several regular grids are a combined into a single irregular sampling pattern. The relative positions of the regular grids are themselves determined by Monte Carlo methods. This generalization obtained by interleaving yields,significantly improved quality compared to traditional approaches while at the same time preserving much of the advantageous coherency of regular sampling. We demonstrate the quality of the new sampling scheme with a number of applications ranging from supersampling over motion blur simulation to volume rendering. Due to the coherence in the interleaved samples, the method is optimally suited for implementations in graphics hardware.
The simulation of random fields has many applications in computer graphics such as e.g. ocean wave or turbulent wind field modeling. We present a new and strikingly simple synthesis algorithm for random fields on rank-1 lattices that requires only one Fourier transform independent of the dimension of the support of the random field. The underlying mathematical principle of discrete Fourier transforms on rank-1 lattices breaks the curse of dimension of the standard tensor product Fourier transform, i.e. the number of function values does not exponentially depend on the dimension, but can be chosen linearly.
As opposed to Monte Carlo integration the quasi-Monte Carlo method does not allow for an (consistent) error estimate from the samples used for the integral approximation. In addition the deterministic error bound of quasi-Monte Carlo integration is not accessible in the setting of computer graphics, since usually the integrands are of unbounded variation. The structure of the high dimensional functionals to be computed for photorealistic image synthesis implies the application of the randomized quasi-Monte Carlo method. Thus we can exploit low discrepancy sampling and at the same time we can estimate the variance. The resulting technique is much more efficient than previous bidirectional path tracing algorithms.
Interactive graphics has been limited to simple direct illumination that commonly results in an artificial appearance. A more realistic appearance by simulating global illumination effects has been too costly to compute at interactive rates. In this paper we describe a new Monte Carlo-based global illumination algorithm. It achieves performance of up to 10 frames per second while arbitrary changes to the scene may be applied interactively. The performance is obtained through the effective use of a fast, distributed ray-tracing engine as well as a new interleaved sampling technique for parallel Monte Carlo simulation. A new filtering step in combination with correlated sampling avoids the disturbing noise artifacts common to Monte Carlo methods.
Image synthesis often requires the Monte Carlo estimation of integrals. Based on a generalized concept of stratification we present an efficient sampling scheme that consistently outperforms previous techniques. This is achieved by assembling sampling patterns that are stratified in the sense of jittered sampling and N-rooks sampling at the same time. The faster convergence and improved anti-aliasing are demonstrated by numerical experiments.
Monte Carlo & Beyond
(2002)
Many rendering problems can only be solved using Monte Carlo integration. The noise and variance inherent with the statistical method efficiently can be reduced by stratification. So far only uncorrelated stratification methods were used that in addition depend on the dimension of the integration domain. Based on rank-1-lattices we present a new stratification technique that removes this dependency on dimension, is much more efficient by correlation, is trivial to implement, and robust to use. The superiority of the new scheme is demonstrated for standard rendering algorithms.
We present an algorithm for determining quadrature rules for computing the direct illumination of predominantly diffuse objects by high dynamic range images. The new method precisely reproduces fine shadow detail, is much more efficient as compared to Monte Carlo integration, and does not require any manual intervention.