Journal article 1538 views
Probabilistic illumination-aware filtering for Monte Carlo rendering
The Visual Computer, Volume: 29, Issue: 6-8, Pages: 707 - 716
Swansea University Author: Mark Jones
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DOI (Published version): 10.1007/s00371-013-0807-3
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...
Published in: | The Visual Computer |
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ISSN: | 0178-2789 1432-2315 |
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2013
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URI: | https://cronfa.swan.ac.uk/Record/cronfa15063 |
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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 31 12 2013 2013-12-31 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 Faculty of Science and Engineering School of Mathematics and Computer 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 |
author2 |
Ian C Doidge Mark Jones |
format |
Journal article |
container_title |
The Visual Computer |
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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 |
Faculty of Science and Engineering |
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|
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
hierarchy_parent_id |
facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
document_store_str |
0 |
active_str |
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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 |
2013-12-31T03:17:12Z |
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1763750374110396416 |
score |
11.028886 |