کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948581 | 1439616 | 2016 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Real-time online learning of Gaussian mixture model for opacity mapping
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Real-time online learning of Gaussian mixture model for opacity mapping Real-time online learning of Gaussian mixture model for opacity mapping](/preview/png/4948581.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 211, 26 October 2016, Pages 212-220
Journal: Neurocomputing - Volume 211, 26 October 2016, Pages 212-220
نویسندگان
Guo Zhou, Dengming Zhu, Yi Wei, Zhaoqi Wang, Yongquan Zhou,