Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

Author:

Huang Jiawei1ORCID,Iizuka Akito2ORCID,Tanaka Hajime2ORCID,Komura Taku3ORCID,Kitamura Yoshifumi2ORCID

Affiliation:

1. Chuzhou University, Chuzhou, China and Void Dimensions, Chuzhou, China

2. Tohoku University, Sendai, Japan

3. The University of Hong Kong, Hong Kong, Hong Kong and Tohoku University, Sendai, Japan

Abstract

Importance sampling techniques significantly reduce variance in physically based rendering. In this article, 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.

Funder

JSPS KAKENHI

Publisher

Association for Computing Machinery (ACM)

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