NDLI: A new refinement criterion for adaptive sampling in path tracing

Content Provider	IEEE Xplore Digital Library
Author	Qing Xu Sbert, M. Feixas, M. Scopigno, R.
Copyright Year	2010
Description	Author affiliation: Networking Lab, Istituto Superiore Mario Boella, 10138 Torino, Italy (Scopigno, R.) \|\| School of Computer Science and Technology, Tianjin Univresity, 300072, China (Qing Xu) \|\| Graphics and Imaging Laboratory, University of Girona, 17071, Spain (Sbert, M.; Feixas, M.)
Abstract	Monte Carlo based methods such as path tracing are the only to do the physically correct simulations of global illumination. Due to its high capability to exploit acceleration structures and SIMD parallelism of modern processors, path tracing is becoming potential in the sense of real time. Basically path tracing generates images with heavy noise. Adaptive sampling is an interesting way to produce less noisy images. In this paper, we make use of both the intra-pixel information and the inter-pixel information to propose a new refinement criterion for adaptive sampling in path tracing to lower the Monte Carlo noise appeared in the generated image. For the intra-pixel information, we take advantage of the non-extensive Tsallis entropy as a homogeneity measurement of sample values within a pixel. For the inter-pixel information, we develop an innovative inter-pixel coherence measure based on the magnitude of spatial gradient and the impulsiveness of the distribution. Implementation results demonstrate that our novel method can perform significantly better than the previously typical ones.
Starting Page	1556
Ending Page	1561
File Size	1318278
Page Count	6
File Format	PDF
ISBN	9781424463909
e-ISBN	9781424463923
DOI	10.1109/ISIE.2010.5636310
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Entropy Coherence Noise measurement Noise Monte Carlo methods Lighting
Content Type	Text
Resource Type	Article

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