“Unbiased, adaptive stochastic sampling for rendering inhomogeneous participating media”
Conference:
Type(s):
Title:
- Unbiased, adaptive stochastic sampling for rendering inhomogeneous participating media
Session/Category Title: Volumetric modeling and rendering
Presenter(s)/Author(s):
Moderator(s):
Abstract:
Realistic rendering of participating media is one of the major subjects in computer graphics. Monte Carlo techniques are widely used for realistic rendering because they provide unbiased solutions, which converge to exact solutions. Methods based on Monte Carlo techniques generate a number of light paths, each of which consists of a set of randomly selected scattering events. Finding a new scattering event requires free path sampling to determine the distance from the previous scattering event, and is usually a time-consuming process for inhomogeneous participating media. To address this problem, we propose an adaptive and unbiased sampling technique using kd-tree based space partitioning. A key contribution of our method is an automatic scheme that partitions the spatial domain into sub-spaces (partitions) based on a cost model that evaluates the expected sampling cost. The magnitude of performance gain obtained by our method becomes larger for more inhomogeneous media, and rises to two orders compared to traditional free path sampling techniques.
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