On learning the visibility for joint importance sampling of low-order scattering

doi:10.1016/j.neucom.2016.09.086

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	On learning the visibility for joint importance sampling of low-order scattering
	Zhou, Guo 1,2,3; Zhu, Dengming 1,2; Li, Ting 4; Wang, Zhaoqi 1,2; Zhou, Yongquan 5
刊名	NEUROCOMPUTING
	2017-03-08
卷号	228 页码:97-105
关键词	Light transport simulation Participating media Online expectation-maximization Importance sampling
ISSN号	0925-2312
DOI	10.1016/j.neucom.2016.09.086
英文摘要	Volumetric path tracing relies on importance sampling to stochastically construct light transport paths from an emitter to the sensor. Existing techniques incrementally sample path vertices or segments with respect to the local scattering property incorporating the geometry and scattering terms. Thus the joint probability density for drawing a path results in a product of the conditional densities each for a local sampling decision. We present a joint path sampling technique that additionally accounts for the spatially varying visibility due to transmittance and occlusion along a double scattering path. The directional density is formulated as a Gaussian mixture model being fitted to single scattered radiance by the online expectation maximization algorithm. It is first trained with samples oblivious to the visibility, then incrementally consumes an arbitrary number of samples being drawn from the actual scene. The resulting density in turn guides the directional sampling decision for both isotropic and anisotropic scattering. We demonstrate the benefit of our approach by integrating it into the unidirectional path tracing algorithm. The image noise is effectively reduced, even while rendering the heterogeneous participating media in the presence of complex opaque surfaces.
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000393017900012
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/7588]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Guo
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.CNCERT CC, Beijing 100029, Peoples R China 5.Guangxi Univ Nationalities, Coll Informat Sci & Engn, Nanning 530006, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Guo,Zhu, Dengming,Li, Ting,et al. On learning the visibility for joint importance sampling of low-order scattering[J]. NEUROCOMPUTING,2017,228:97-105.
APA	Zhou, Guo,Zhu, Dengming,Li, Ting,Wang, Zhaoqi,&Zhou, Yongquan.(2017).On learning the visibility for joint importance sampling of low-order scattering.NEUROCOMPUTING,228,97-105.
MLA	Zhou, Guo,et al."On learning the visibility for joint importance sampling of low-order scattering".NEUROCOMPUTING 228(2017):97-105.