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- research-articleJune 2024
Joint self-attention for denoising Monte Carlo rendering
The Visual Computer: International Journal of Computer Graphics (VISC), Volume 40, Issue 7Jul 2024, Pages 4623–4634https://doi.org/10.1007/s00371-024-03446-8AbstractImage-space denoising of rendered images has become a commonly adopted approach since this post-rendering process often drastically reduces required sample counts (thus rendering times) for producing a visually pleasing image without noticeable ...
- research-articleApril 2024
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
ACM Transactions on Graphics (TOG), Volume 43, Issue 3Article No.: 26, Pages 1–18https://doi.org/10.1145/3649310Importance sampling techniques significantly reduce variance in physically based rendering. In this article, we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small ...
- research-articleDecember 2023
Perceptual error optimization for Monte Carlo animation rendering
- Miša Korać,
- Corentin Salaün,
- Iliyan Georgiev,
- Pascal Grittmann,
- Philipp Slusallek,
- Karol Myszkowski,
- Gurprit Singh
SA '23: SIGGRAPH Asia 2023 Conference PapersDecember 2023, Article No.: 89, Pages 1–10https://doi.org/10.1145/3610548.3618146Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that perceptual fidelity can be improved significantly by distributing pixel ...
- research-articleAugust 2022
Multi-Scale and Kernel-Predicting Convolutional Networks for Monte Carlo Denoising
PRIS '22: Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent SystemsJuly 2022, Pages 23–26https://doi.org/10.1145/3549179.3549183Monte Carlo rendering has been widely used in many fields, such as movies, which pursue the photorealistic rendering effect. Monte Carlo rendering needs high sampling rates to get an accurate rendering effect, but the calculation cost is expensive. To ...
- posterJuly 2022
Denoising and Guided Upsampling of Monte Carlo Path Traced Low Resolution Renderings
SIGGRAPH '22: ACM SIGGRAPH 2022 PostersJuly 2022, Article No.: 37, Pages 1–2https://doi.org/10.1145/3532719.3543250Monte Carlo path tracing generates renderings by estimating the rendering equation using the Monte Carlo method. Studies focus on rendering a noisy image at the original resolution with a low sample per pixel count to decrease the rendering time. Image-...
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- research-articleDecember 2021
Differentiable transient rendering
ACM Transactions on Graphics (TOG), Volume 40, Issue 6Article No.: 286, Pages 1–11https://doi.org/10.1145/3478513.3480498Recent differentiable rendering techniques have become key tools to tackle many inverse problems in graphics and vision. Existing models, however, assume steady-state light transport, i.e., infinite speed of light. While this is a safe assumption for ...
- research-articleJuly 2021
Path-space differentiable rendering of participating media
ACM Transactions on Graphics (TOG), Volume 40, Issue 4Article No.: 76, Pages 1–15https://doi.org/10.1145/3450626.3459782Physics-based differentiable rendering---which focuses on estimating derivatives of radiometric detector responses with respect to arbitrary scene parameters---has a diverse array of applications from solving analysis-by-synthesis problems to training ...
- research-articleNovember 2020
Rendering near-field speckle statistics in scattering media
ACM Transactions on Graphics (TOG), Volume 39, Issue 6Article No.: 187, Pages 1–18https://doi.org/10.1145/3414685.3417813We introduce rendering algorithms for the simulation of speckle statistics observed in scattering media under coherent near-field imaging conditions. Our work is motivated by the recent proliferation of techniques that use speckle correlations for ...
- research-articleAugust 2020
Path-space differentiable rendering
ACM Transactions on Graphics (TOG), Volume 39, Issue 4Article No.: 143, Pages 143:1–143:19https://doi.org/10.1145/3386569.3392383Physics-based differentiable rendering, the estimation of derivatives of radiometric measures with respect to arbitrary scene parameters, has a diverse array of applications from solving analysis-by-synthesis problems to training machine learning ...
- research-articleSeptember 2019
DEMC: A Deep Dual-Encoder Network for Denoising Monte Carlo Rendering
Journal of Computer Science and Technology (JCST), Volume 34, Issue 5Sep 2019, Pages 1123–1135https://doi.org/10.1007/s11390-019-1964-2AbstractIn this paper, we present DEMC, a deep dual-encoder network to remove Monte Carlo noise efficiently while preserving details. Denoising Monte Carlo rendering is different from natural image denoising since inexpensive by-products (feature ...
- research-articleDecember 2018
Robust deep residual denoising for Monte Carlo rendering
SA '18: SIGGRAPH Asia 2018 Technical BriefsDecember 2018, Article No.: 14, Pages 1–4https://doi.org/10.1145/3283254.3283261We 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 ...
- articleJune 2018
A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo rendering
The Visual Computer: International Journal of Computer Graphics (VISC), Volume 34, Issue 6-8Jun 2018, Pages 765–778https://doi.org/10.1007/s00371-018-1521-yAlthough many adaptive sampling and reconstruction techniques for Monte Carlo (MC) rendering have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique ...
- research-articleNovember 2017
Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks
ACM Transactions on Graphics (TOG), Volume 36, Issue 6Article No.: 231, Pages 1–11https://doi.org/10.1145/3130800.3130880We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of ...
- invited-talkJuly 2017
Double hierarchies for efficient sampling in Monte Carlo rendering
SIGGRAPH '17: ACM SIGGRAPH 2017 TalksJuly 2017, Article No.: 36, Pages 1–2https://doi.org/10.1145/3084363.3085063We propose a novel representation of the light field tailored to improve importance sampling for Monte Carlo rendering. The domain of the light field i.e., the product space of spatial positions and directions is hierarchically subdivided into subsets ...
- research-articleJuly 2017
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
- Steve Bako,
- Thijs Vogels,
- Brian Mcwilliams,
- Mark Meyer,
- Jan NováK,
- Alex Harvill,
- Pradeep Sen,
- Tony Derose,
- Fabrice Rousselle
ACM Transactions on Graphics (TOG), Volume 36, Issue 4Article No.: 97, Pages 1–14https://doi.org/10.1145/3072959.3073708Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit ...
- articleJune 2017
Analysis of reported error in Monte Carlo rendered images
The Visual Computer: International Journal of Computer Graphics (VISC), Volume 33, Issue 6-8June 2017, Pages 705–713https://doi.org/10.1007/s00371-017-1384-7Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The ...
- articleFebruary 2016
Projective Blue-Noise Sampling
Computer Graphics Forum (COMGRAFOR), Volume 35, Issue 1February 2016, Pages 285–295https://doi.org/10.1111/cgf.12725We propose projective blue-noise patterns that retain their blue-noise characteristics when undergoing one or multiple projections onto lower dimensional subspaces. These patterns are produced by extending existing methods, such as dart throwing and ...
- research-articleJuly 2015
A machine learning approach for filtering Monte Carlo noise
ACM Transactions on Graphics (TOG), Volume 34, Issue 4Article No.: 122, Pages 1–12https://doi.org/10.1145/2766977The most successful approaches for filtering Monte Carlo noise use feature-based filters (e.g., cross-bilateral and cross non-local means filters) that exploit additional scene features such as world positions and shading normals. However, their main ...
- research-articleFebruary 2014
Boosting monte carlo rendering by ray histogram fusion
ACM Transactions on Graphics (TOG), Volume 33, Issue 1Article No.: 8, Pages 1–15https://doi.org/10.1145/2532708This article proposes a new multiscale filter accelerating Monte Carlo renderer. Each pixel in the image is characterized by the colors of the rays that reach its surface. The proposed filter uses a statistical distance to compare with each other the ...
- ArticleNovember 2013
P-RPF: Pixel-Based Random Parameter Filtering for Monte Carlo Rendering
CADGRAPHICS '13: Proceedings of the 2013 International Conference on Computer-Aided Design and Computer GraphicsNovember 2013, Pages 123–130https://doi.org/10.1109/CADGraphics.2013.24In this paper we propose Pixel-based Random Parameter Filtering (P-RPF) for efficiently denoising images generated from complex illuminations with a high sample count. We design various operations of our method to have time complexity that is ...