## Fachbereich Informatik

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Monte Carlo & Beyond
(2002)

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.

Instant Radiosity
(1997)

We present a fundamental procedure for instant rendering from the radiance equation. Operating directly on the textured scene description, the very efficient and simple algorithm produces photorealistic images without any kernel or solution discretization of the underlying integral equation. Rendering rates of a few seconds are obtained by exploiting graphics hardware, the deterministic
technique of the quasi-random walk for the solution of the global illumination problem, and the new method of jittered low discrepancy sampling.

A fundamental variance reduction technique for Monte Carlo integration in the framework of integro-approximation problems is
presented. Using the method of dependent tests a successive hierarchical function approximation algorithm is developed, which
captures discontinuities and exploits smoothness in the target function. The general mathematical scheme and its highly efficient
implementation are illustrated for image generation by ray tracing,
yielding new and much faster image synthesis algorithms.

The photon map provides a powerful tool for approximating the irradiance in global illumination computations independent from geometry. By presenting new importance sampling techniques, we dramatically improve the memory footprint of the photon map, simplify the caustic generation, and allow for a much faster sampling of direct illumination in complicated models as they arise in a production environment.

The main problem in computer graphics is to solve the global illumination problem,
which is given by a Fredholm integral equation of the second kind, called the radiance equation (REQ). In order to achieve realistic images, a very complex kernel
of the integral equation, modelling all physical effects of light, must be considered. Due to this complexity Monte Carlo methods seem to be an appropriate approach to solve the REQ approximately. We show that replacing Monte Carlo by quasi-Monte Carlo in some steps of the algorithm results in a faster convergence.

The radiance equation, which describes the global illumination problem in computer graphics, is a high dimensional integral equation. Estimates of the solution are usually computed on the basis of Monte Carlo methods. In this paper we propose and investigate quasi-Monte Carlo methods, which means that we replace (pseudo-) random samples by low discrepancy sequences, yielding deterministic algorithms. We carry out a comparative numerical study between Monte Carlo and quasi-Monte Carlo methods. Our results show that quasi-Monte Carlo converges considerably faster.

Monte Carlo integration is often used for antialiasing in rendering processes.
Due to low sampling rates only expected error estimates can be stated, and the variance can be high. In this article quasi-Monte Carlo methods are presented, achieving a guaranteed upper error bound and a convergence rate essentially as fast as usual Monte Carlo.

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

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.