Statistical Error Reduction for Monte Carlo Rendering

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Toward Optimal Path Guiding for Photorealistic Rendering
• Date: December 2025
• ISBN: 979-8-4007-2137-3
• Publisher: Association for Computing Machinery
• Open Access: yes
• Location: Hong Kong
• Lecturer: Hiroyuki Sakai
• Event: SA '25: SIGGRAPH Asia 2025
• DOI: 10.1145/3757377.3763995
• Booktitle: SA '25: SIGGRAPH Asia 2025 Conference Papers
• Conference date: 15. December 2025 – 18. December 2025
• Keywords: Monte Carlo rendering, path tracing, denoising, image filtering, statistics

Abstract

Denoising is an important post-processing step in physically based Monte Carlo (MC) rendering. While neural networks are widely used in practice, statistical analysis has recently become a viable alternative for denoising. In this paper, we present a general framework for statistics-based error reduction of both estimated radiance and variance. Specifically, we introduce a novel denoising approach for variance estimates, which can either improve variance-aware adaptive sampling or provide additional input for image denoising in a cascaded manner. Furthermore, we present multi-transform denoising: a general and efficient correction scheme for non-normal distributions, which typically occur in MC rendering. All these contributions combine to a robust denoising pipeline that does not require any pretraining and can run efficiently on current GPU hardware. Our results show distinct advantages over previous denoising methods, especially in the range of a few hundred samples per pixel, which is of high practical relevance. Finally, we demonstrate good convergence behavior as the number of samples increases, providing predictable results with low bias that are free of hallucinated neural artifacts. In summary, our statistics-based algorithms for adaptive sampling and denoising deliver fast, consistent, low-bias variance and radiance estimates.

Additional Files and Images

Weblinks

• Project Page
• DOI: 10.1145/3757377.3763995

BibTeX

@inproceedings{sakai-2025-stater,
  title =      "Statistical Error Reduction for Monte Carlo Rendering",
  author =     "Hiroyuki Sakai and Christian Freude and Michael Wimmer and
               David Hahn",
  year =       "2025",
  abstract =   "Denoising is an important post-processing step in physically
               based Monte Carlo (MC) rendering. While neural networks are
               widely used in practice, statistical analysis has recently
               become a viable alternative for denoising. In this paper, we
               present a general framework for statistics-based error
               reduction of both estimated radiance and variance.
               Specifically, we introduce a novel denoising approach for
               variance estimates, which can either improve variance-aware
               adaptive sampling or provide additional input for image
               denoising in a cascaded manner. Furthermore, we present
               multi-transform denoising: a general and efficient
               correction scheme for non-normal distributions, which
               typically occur in MC rendering. All these contributions
               combine to a robust denoising pipeline that does not require
               any pretraining and can run efficiently on current GPU
               hardware. Our results show distinct advantages over previous
               denoising methods, especially in the range of a few hundred
               samples per pixel, which is of high practical relevance.
               Finally, we demonstrate good convergence behavior as the
               number of samples increases, providing predictable results
               with low bias that are free of hallucinated neural
               artifacts. In summary, our statistics-based algorithms for
               adaptive sampling and denoising deliver fast, consistent,
               low-bias variance and radiance estimates.",
  month =      dec,
  isbn =       "979-8-4007-2137-3",
  publisher =  "Association for Computing Machinery",
  location =   "Hong Kong",
  event =      "SA '25: SIGGRAPH Asia 2025",
  doi =        "10.1145/3757377.3763995",
  booktitle =  "SA '25: SIGGRAPH Asia 2025 Conference Papers",
  keywords =   "Monte Carlo rendering, path tracing, denoising, image
               filtering, statistics",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/sakai-2025-stater/",
}