Real-time online learning of Gaussian mixture model for opacity mapping

doi:10.1016/j.neucom.2015.12.135

	Real-time online learning of Gaussian mixture model for opacity mapping
	Zhou, Guo 1,2,3; Zhu, Dengming 1,2; Wei, Yi 1,2; Wang, Zhaoqi 1,2; Zhou, Yongquan 4
	2016-10-26
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	211 页码:212-220
摘要	Rendering volumetric scattering in real-time is a challenge due to the complex interactions between the light and the particles in the participating media. Assuming that a ray leaving the emitter is scattered only once along its path to the sensor, we propose to represent the extinction coefficient by a Gaussian mixture model. Then the model is trained with a large number of particles colliding that ray in an online way. A low-cost updating function based on the weighted maximum likelihood estimation is derived for the weighted stepwise expectation-maximization algorithm, which is fitted into the graphics pipeline as a stage of learning. This enables all those particles to contribute to the extinction on the fly without storing and sorting them together with respect to the emitter in a geometry pass. Our approach is able to accurately reconstruct the per-pixel transmittance of the opacity map for optically thick heterogeneous media in real-time but operate in bounded memory, using the recently introduced fragment shader critical section feature of the graphics processing unit. (C) 2016 Elsevier B.V. All rights reserved.
关键词	Participating media Online expectation-maximization Shadow Order-independent transparency
DOI	10.1016/j.neucom.2015.12.135
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000384871700023
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7971
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.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,Wei, Yi,et al. Real-time online learning of Gaussian mixture model for opacity mapping[J]. NEUROCOMPUTING,2016,211:212-220.
APA	Zhou, Guo,Zhu, Dengming,Wei, Yi,Wang, Zhaoqi,&Zhou, Yongquan.(2016).Real-time online learning of Gaussian mixture model for opacity mapping.NEUROCOMPUTING,211,212-220.
MLA	Zhou, Guo,et al."Real-time online learning of Gaussian mixture model for opacity mapping".NEUROCOMPUTING 211(2016):212-220.