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Probabilistic illumination-aware filtering for Monte Carlo rendering / Mark, Jones

The Visual Computer, Volume: 29, Issue: 6-8, Pages: 707 - 716

Swansea University Author: Mark, Jones

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Abstract

Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we pre...

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Published in: The Visual Computer
ISSN: 0178-2789 1432-2315
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa15063
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last_indexed 2018-02-09T04:46:46Z
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spelling 2013-12-04T13:14:32.9075107 v2 15063 2013-06-13 Probabilistic illumination-aware filtering for Monte Carlo rendering 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2013-06-13 SCS Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we present a probabilistic image based noise removal and irradiance filtering framework that preserves this high frequency detail such as hard shadows and glossy reflections, and imposes no restrictions on the characteristics of the light transport or materials. We maintain per-pixel clusters of the path traced samples and, using statistics from these clusters, derive an illumination aware filtering scheme based on the discrete Poisson probability distribution. Furthermore, we filter the incident radiance of the samples, allowing us to preserve and filter across high frequency and complex textures without limiting the effectiveness of the filter. Journal Article The Visual Computer 29 6-8 707 716 0178-2789 1432-2315 1 1 2013 2013-01-01 10.1007/s00371-013-0807-3 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2013-12-04T13:14:32.9075107 2013-06-13T13:58:00.8783237 College of Science Computer Science Ian C Doidge 1 Mark Jones 0000-0001-8991-1190 2
title Probabilistic illumination-aware filtering for Monte Carlo rendering
spellingShingle Probabilistic illumination-aware filtering for Monte Carlo rendering
Mark, Jones
title_short Probabilistic illumination-aware filtering for Monte Carlo rendering
title_full Probabilistic illumination-aware filtering for Monte Carlo rendering
title_fullStr Probabilistic illumination-aware filtering for Monte Carlo rendering
title_full_unstemmed Probabilistic illumination-aware filtering for Monte Carlo rendering
title_sort Probabilistic illumination-aware filtering for Monte Carlo rendering
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark, Jones
author Mark, Jones
format Journal article
container_title The Visual Computer
container_volume 29
container_issue 6-8
container_start_page 707
publishDate 2013
institution Swansea University
issn 0178-2789
1432-2315
doi_str_mv 10.1007/s00371-013-0807-3
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we present a probabilistic image based noise removal and irradiance filtering framework that preserves this high frequency detail such as hard shadows and glossy reflections, and imposes no restrictions on the characteristics of the light transport or materials. We maintain per-pixel clusters of the path traced samples and, using statistics from these clusters, derive an illumination aware filtering scheme based on the discrete Poisson probability distribution. Furthermore, we filter the incident radiance of the samples, allowing us to preserve and filter across high frequency and complex textures without limiting the effectiveness of the filter.
published_date 2013-01-01T12:27:04Z
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score 10.867679