用归一化各向异性球形高斯进行在线神经路径引导

IF 7.8 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
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引用次数: 0

摘要

重要性采样技术能显著减少基于物理的渲染中的差异。在本文中,我们提出了一个新颖的在线框架,利用随机光线采样,通过单个小型神经网络学习渲染方程全乘积的空间变化分布。学习到的分布可用于高效采样入射光的全积。为了实现这一目标,我们引入了一种新颖的闭式密度模型,称为归一化各向异性球形高斯混合物,它可以用少量参数对复杂光场进行建模,并可直接采样。我们的框架可以逐步渲染和学习该分布,无需任何预热阶段。由于我们的密度模型结构紧凑、表现力强,因此我们的框架可以完全在 GPU 上实现,从而可以利用有限的计算资源生成高质量的图像。结果表明,我们的框架优于现有的神经路径引导方法,其性能与最先进的在线统计路径引导技术相当,甚至更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

Importance sampling techniques significantly reduce variance in physically-based rendering. In this paper we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish this, we introduce a novel closed-form density model, called the Normalized Anisotropic Spherical Gaussian mixture, that can model a complex light field with a small number of parameters and that can be directly sampled. Our framework progressively renders and learns the distribution, without requiring any warm-up phases. With the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it to produce high-quality images with limited computational resources. The results show that our framework outperforms existing neural path guiding approaches and achieves comparable or even better performance than state-of-the-art online statistical path guiding techniques.

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来源期刊
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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