Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914692606197760 |
|---|---|
| author | Huang, Jiawei Iizuka, Akito Tanaka, Hajime Komura, Taku Kitamura, Yoshifumi |
| author_facet | Huang, Jiawei Iizuka, Akito Tanaka, Hajime Komura, Taku Kitamura, Yoshifumi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08064 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians Huang, Jiawei Iizuka, Akito Tanaka, Hajime Komura, Taku Kitamura, Yoshifumi Computer Vision and Pattern Recognition Graphics I.3 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. |
| title | Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians |
| topic | Computer Vision and Pattern Recognition Graphics I.3 |
| url | https://arxiv.org/abs/2303.08064 |