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Photon Parameterisation for Robust Relaxation Constraints / Mark, Jones

Computer Graphics Forum, Volume: 32, Issue: 2pt1, Pages: 83 - 92

Swansea University Author: Mark, Jones

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DOI (Published version): 10.1111/cgf.12028

Abstract

This paper presents a novel approach to detecting and preserving fine illumination structure within photon maps. Data derived from each photon's primal trajectory is encoded and used to build a high-dimensional kd-tree. Incorporation of these new parameters allows for precise differentiatio...

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Published in: Computer Graphics Forum
ISSN: 0167-7055
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa14358
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first_indexed 2013-07-23T12:12:06Z
last_indexed 2019-06-21T13:18:45Z
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spelling 2019-06-21T12:28:26.4947171 v2 14358 2013-02-21 Photon Parameterisation for Robust Relaxation Constraints 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2013-02-21 SCS This paper presents a novel approach to detecting and preserving fine illumination structure within photon maps. Data derived from each photon&apos;s primal trajectory is encoded and used to build a high-dimensional kd-tree. Incorporation of these new parameters allows for precise differentiation between intersecting ray envelopes, thus minimizing detail degradation when combined with photon relaxation. We demonstrate how parameter-aware querying is beneficial in both detecting and removing noise. We also propose a more robust structure descriptor based on principal components analysis that better identifies anisotropic detail at the sub-kernel level. We illustrate the effectiveness of our approach in several example scenes and show significant improvements when rendering complex caustics compared to previous methods. Journal Article Computer Graphics Forum 32 2pt1 83 92 0167-7055 7 5 2013 2013-05-07 10.1111/cgf.12028 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University RCUK, EP/I031243/1 2019-06-21T12:28:26.4947171 2013-02-21T15:22:39.0547616 College of Science Computer Science B Spencer 1 M.W Jones 2 Mark Jones 0000-0001-8991-1190 3
title Photon Parameterisation for Robust Relaxation Constraints
spellingShingle Photon Parameterisation for Robust Relaxation Constraints
Mark, Jones
title_short Photon Parameterisation for Robust Relaxation Constraints
title_full Photon Parameterisation for Robust Relaxation Constraints
title_fullStr Photon Parameterisation for Robust Relaxation Constraints
title_full_unstemmed Photon Parameterisation for Robust Relaxation Constraints
title_sort Photon Parameterisation for Robust Relaxation Constraints
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark, Jones
author Mark, Jones
format Journal article
container_title Computer Graphics Forum
container_volume 32
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container_start_page 83
publishDate 2013
institution Swansea University
issn 0167-7055
doi_str_mv 10.1111/cgf.12028
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 This paper presents a novel approach to detecting and preserving fine illumination structure within photon maps. Data derived from each photon&apos;s primal trajectory is encoded and used to build a high-dimensional kd-tree. Incorporation of these new parameters allows for precise differentiation between intersecting ray envelopes, thus minimizing detail degradation when combined with photon relaxation. We demonstrate how parameter-aware querying is beneficial in both detecting and removing noise. We also propose a more robust structure descriptor based on principal components analysis that better identifies anisotropic detail at the sub-kernel level. We illustrate the effectiveness of our approach in several example scenes and show significant improvements when rendering complex caustics compared to previous methods.
published_date 2013-05-07T19:26:45Z
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score 10.736045