التفاصيل البيبلوغرافية
| العنوان: |
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians. |
| المؤلفون: |
JIAWEI HUANG, AKITO IIZUKA, HAJIME TANAKA, TAKU KOMURA, YOSHIFUMI KITAMURA |
| المصدر: |
ACM Transactions on Graphics; Jun2024, Vol. 43 Issue 3, p1-18, 18p |
| مصطلحات موضوعية: |
RENDERING (Computer graphics), ONLINE education, SAMPLING (Process), WARMUP |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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