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

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• 作者：Guo Zhou^a ; ^b ; ^c ; ^{zhouguo@ict.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Dengming Zhu^a ; ^bAuthor Vitae ; Yi Wei^a ; ^bAuthor Vitae ; Zhaoqi Wang^a ; ^bAuthor Vitae ; Yongquan Zhou^dAuthor Vitae
• 关键词：Participating media ; Online expectation&ndash ; maximization ; Shadow ; Order-independent transparency
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：26 October 2016
• 年：2016
• 卷：211
• 期：Complete
• 页码：212-220
• 全文大小：1141 K

文摘

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.