Converging Algorithm-Agnostic Denoising for Monte Carlo Rendering
Abstract
Supplemental Material
- Download
- 6.67 MB
References
Index Terms
- Converging Algorithm-Agnostic Denoising for Monte Carlo Rendering
Recommendations
Robust deep residual denoising for Monte Carlo rendering
SA '18: SIGGRAPH Asia 2018 Technical BriefsWe propose a Deep Residual Learning based method that consistently outperforms both the state-of-the-art handcrafted denoisers and learning-based methods for single-image Monte Carlo denoising. Unlike the indirect nature of existing learning-based ...
MCNeRF: Monte Carlo Rendering and Denoising for Real-Time NeRFs
SA '23: SIGGRAPH Asia 2023 Conference PapersThe volume rendering step used in Neural Radiance Fields (NeRFs) produces highly photorealistic results, but is inherently slow because it evaluates an MLP at a large number of sample points per ray. Previous work has addressed this by either proposing ...
Target-Aware Image Denoising for Inverse Monte Carlo Rendering
Physically based differentiable rendering allows an accurate light transport simulation to be differentiated with respect to the rendering input, i.e., scene parameters, and it enables inferring scene parameters from target images, e.g., photos or ...
Comments
Information & Contributors
Information
Published In
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 2Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)2
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in