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Journal article 1347 views

Progressive photon relaxation

Ben Spencer, Mark Jones Orcid Logo

ACM Transactions on Graphics, Volume: 32, Issue: 1, Pages: 1 - 11

Swansea University Author: Mark Jones Orcid Logo

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DOI (Published version): 10.1145/2421636.2421643

Abstract

We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this...

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Published in: ACM Transactions on Graphics
ISSN: 0730-0301
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa14350
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first_indexed 2013-07-23T12:12:06Z
last_indexed 2018-02-09T04:45:43Z
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spelling 2015-07-08T13:39:59.4420646 v2 14350 2013-02-20 Progressive photon relaxation 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2013-02-20 SCS We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping. Journal Article ACM Transactions on Graphics 32 1 1 11 0730-0301 31 1 2013 2013-01-31 10.1145/2421636.2421643 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2015-07-08T13:39:59.4420646 2013-02-20T11:19:15.5853796 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ben Spencer 1 Mark Jones 0000-0001-8991-1190 2
title Progressive photon relaxation
spellingShingle Progressive photon relaxation
Mark Jones
title_short Progressive photon relaxation
title_full Progressive photon relaxation
title_fullStr Progressive photon relaxation
title_full_unstemmed Progressive photon relaxation
title_sort Progressive photon relaxation
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
author Mark Jones
author2 Ben Spencer
Mark Jones
format Journal article
container_title ACM Transactions on Graphics
container_volume 32
container_issue 1
container_start_page 1
publishDate 2013
institution Swansea University
issn 0730-0301
doi_str_mv 10.1145/2421636.2421643
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title 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
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description We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping.
published_date 2013-01-31T03:16:28Z
_version_ 1763750327876583424
score 11.013148

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