Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

• URL: http://arxiv.org/abs/2303.08064v2
• Date: Tue, 27 Feb 2024 05:32:03 GMT
• Title: Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
• Authors: Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
• Abstract summary: We propose a novel online framework to learn the spatial-varying density model with a single small neural network. Our framework learns the distribution in a progressive manner and does not need any warm-up phases.
• Score: 20.68953631807367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique. In this paper we propose a novel online framework to learn the spatial-varying density model with a single small neural network using stochastic ray samples. To achieve this task, we propose a novel closed-form density model called the normalized anisotropic spherical gaussian mixture, that can express complex irradiance fields with a small number of parameters. Our framework learns the distribution in a progressive manner and does not need any warm-up phases. Due to the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it produce high quality images with limited computational resources.

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