Parametric Integration with Neural Integral Operators
Abstrak
Real-time rendering imposes strict limitations on the sampling budget for light transport simulation, often resulting in noisy images. However, denoisers have demonstrated that it is possible to produce noise-free images through filtering. We enhance image quality by removing noise before material shading, rather than filtering already shaded noisy images. This approach allows for material-agnostic denoising (MAD) and leverages machine learning by approximating the light transport integral operator with a neural network, effectively performing parametric integration with neural operators. Our method operates in real-time, requires data from only a single frame, seamlessly integrates with existing denoisers and temporal anti-aliasing techniques, and is efficient to train. Additionally, it is straightforward to incorporate with physically based rendering algorithms.
Topik & Kata Kunci
Penulis (2)
Christoph Schied
Alexander Keller
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓