@article{mldenoisingkernelpredicting2025,
  title = {Production Machine-Learning Denoising: Kernel-Predicting Networks},
  author = {Vogels, T. and Rousselle, F. and McWilliams, B. and Roethlin, G. and Harvill, A. and Adler, D. and Meyer, M. and Novak, J.},
  year = {2018},
  journal = {ACM Transactions on Graphics (SIGGRAPH 2018)},
  url = {https://studios.disneyresearch.com/2018/07/30/denoising-with-kernel-prediction-and-asymmetric-loss-functions/},
  abstract = {Vogels et al. (2018) and the preceding Bako et al. (2017) KPCN work introduce kernel-predicting convolutional networks for Monte Carlo render denoising, predicting a spatially varying denoising kernel at each pixel from noisy input and auxiliary feature buffers such as normals and albedo. The approach allows production renders to use far fewer samples per pixel while achieving final-frame quality, with ML inference replacing expensive sample accumulation. An April 2025 Academy Scientific and Engineering Award recognised the Disney Research KPAL work, Intel Open Image Denoise (integrated into Blender, Arnold, and V-Ray), and the Weta FX denoiser as a category, confirming that ML denoising has become an invisible production standard in CG feature pipelines globally.},
  keywords = {vfx-production, production-practice},
  note = {AI \& Animation Education Knowledge Base}
}