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Progressive Path Tracing with Lightweight Local Error Estimation

MPG-Autoren

Dmitriev,  Kirill
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44965

Magnor,  Marcus
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Dmitriev, K., & Seidel, H.-P. (2004). Progressive Path Tracing with Lightweight Local Error Estimation. In Vision, modeling, and visualization 2004 (VMV-04) (pp. 249-254). Berlin, Germany: Akademische Verlagsgesellschaft Aka.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-2B05-C
Zusammenfassung
Adaptive sampling techniques typically applied in path tracing are not progressive. The reason is that they need all the samples used to compute pixel color for error estimation. Thus progressive computation would need to store all the samples for all the pixels, which is too expensive. Absence of progressivity is a big disadvantage of adaptive path tracing algorithms because a user may become aware of some unwanted effects on the image only after quite significant time. We propose a new estimate of local error in path tracing. The new technique happens to be lightweight in terms of both memory and execution time and lends itself very well to progressivity. Also, even thought perceptual error metric is used, it allows changes of any tone mapping parameters during the course of computation. In this case none of the previous effort is lost, error distribution is immediately updated and used for refining the solution.