Particle transport and image synthesis

We present a new method for estimating the radiance function of complex area light sources. The method is based on Jensen's photon mapping algorithm. In order to capture high angular frequencies in the radiance function, we incorporate the angular domain into the density estimation. However, density estimation in position-direction space makes it necessary to find a tradeoff between the spatial and angular accuracy of the estimation. We identify the parameters which are important for this tradeoff and investigate the typical estimation errors. We show how the large data size, which is inherent to the underlying problem, can be handled. The method is applied to different automotive tail lights. It can be applied to a wide range of other real-world light sources.

Section: Photon Tracingmentioning

confidence: 99%

Efficient and Accurate Rendering of Complex Light Sources

Kniep

Häring

Magnor

2009

“…Adaptive sampling done in this way can optimally take advantage of the variance reducing qualities of stratification. Of course the right distribution of samples and the correct normalization have to be taken into account to avoid bias (Arvo and Kirk, 1990;Kirk and Arvo, 1991).…”

Section: Figure 2 Stratification and Adaptive Samplingmentioning

confidence: 99%

Adaptive Sampling of Area Light Sources in Ray Tracing Including Diffuse Interreflection

Kok¹,

Jansen²

1992

Ray tracing algorithms that sample both the light received directly from light sources and the light received indirectly by diffuse reflection from other patches, can accurately render the global illumination in a scene and can display complex scenes with accurate shadowing. A drawback of these algorithms, however, is the high cost for sampling the direct light which is done by shadow ray testing. Although several strategies are available to reduce the number of shadow rays, still a large number of rays will be needed, in particular to sample large area light sources. An adaptive sampling strategy is proposed that reduces the number of shadow rays by using statistical information from the sampling process and by applying information from a radiosity preprocessing. A further reduction in shadow rays is obtained by applying shadow pattern coherence, i.e. reusing the adaptive sampling pattern for neighboring sampling points.

“…[Kaj86] formulated photorealistic rendering as the rendering equation and then presented the classical path-tracing algorithm, which is the first unbiased Monte Carlo rendering algorithm. Since then many refinements have been proposed, e.g., [AK90] and [SKAS05]. Often these improvements have been adapted from neutron transport and radiative heat transfer literatures, which have a long history of solving similar transport problems.…”

Section: Introductionmentioning

confidence: 99%

Replica Exchange Light Transport

Kitaoka¹,

Kitamura²,

Kishino³

2009

We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replicaexchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain MonteCarlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport.