research-article

Boosting monte carlo rendering by ray histogram fusion

Authors:

Mauricio Delbracio,

Julien Chauvier,

Nicholas Phelps, and

Jean-Michel MorelAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 33, Issue 1

Article No.: 8, Pages 1 - 15

https://doi.org/10.1145/2532708

Published: 07 February 2014 Publication History

Abstract

This 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 ray color distributions associated with different pixels, at each scale. Based on this distance, it decides whether two pixels can share their rays or not. This simple and easily reproducible algorithm provides a psnr gain of 10 to 15 decibels, or equivalently accelerates the rendering process by using 10 to 30 times fewer samples without observable bias. The algorithm is consistent, does not assume a particular noise model, and is immediately extendable to synthetic movies. Being based on the ray color values only, it can be combined with all rendering effects.

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Cited By

Shu ZYi RMeng YWu YMa L(2023)RT-Octree: Accelerate PlenOctree Rendering with Batched Regular Tracking and Neural Denoising for Real-time Neural Radiance FieldsSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618214(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618214
Balint MWolski KMyszkowski KSeidel HMantiuk R(2023)Neural Partitioning Pyramids for Denoising Monte Carlo RenderingsACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591562(1-11)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3588432.3591562
Li LZhang PSmirnov DAbulnaga SSolomon J(2021)Interactive all-hex meshing via cuboid decompositionACM Transactions on Graphics10.1145/3478513.348056840:6(1-17)Online publication date: 10-Dec-2021
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Index Terms

Boosting monte carlo rendering by ray histogram fusion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
    2. Rendering
      1. Ray tracing

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 33, Issue 1

January 2014

179 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2577382

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 07 February 2014

Accepted: 01 July 2013

Revised: 01 May 2013

Received: 01 November 2012

Published in TOG Volume 33, Issue 1

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Office of Naval Research
Region de France (CAP DIGITAL, DIGIAAPFEDER 4)
European Research Council

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Cited By

Shu ZYi RMeng YWu YMa L(2023)RT-Octree: Accelerate PlenOctree Rendering with Batched Regular Tracking and Neural Denoising for Real-time Neural Radiance FieldsSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618214(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618214
Balint MWolski KMyszkowski KSeidel HMantiuk R(2023)Neural Partitioning Pyramids for Denoising Monte Carlo RenderingsACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591562(1-11)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3588432.3591562
Li LZhang PSmirnov DAbulnaga SSolomon J(2021)Interactive all-hex meshing via cuboid decompositionACM Transactions on Graphics10.1145/3478513.348056840:6(1-17)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480568
Yu JNie YLong CXu WZhang QLi G(2021)Monte Carlo denoising via auxiliary feature guided self-attentionACM Transactions on Graphics10.1145/3478513.348056540:6(1-13)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480565
Cho IHuo YYoon S(2021)Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstructionACM Transactions on Graphics10.1145/3450626.345987640:4(1-14)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459876
Işık MMullia KFisher MEisenmann JGharbi M(2021)Interactive Monte Carlo denoising using affinity of neural featuresACM Transactions on Graphics10.1145/3450626.345979340:4(1-13)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459793
Xing QChen CLi Z(2021)Accelerated Path Tracing With GAN and Matrix CompletionIEEE Access10.1109/ACCESS.2021.30598879(39055-39066)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3059887
Henter GAlexanderson SBeskow J(2020)MoGlowACM Transactions on Graphics10.1145/3414685.341783639:6(1-14)Online publication date: 27-Nov-2020
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Loubet GZeltner THolzschuch NJakob W(2020)Slope-space integrals for specular next event estimationACM Transactions on Graphics10.1145/3414685.341781139:6(1-13)Online publication date: 27-Nov-2020
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Wang BHašan MYan L(2020)Path cutsACM Transactions on Graphics10.1145/3414685.341779239:6(1-12)Online publication date: 27-Nov-2020
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