Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-time video based lighting using GPU raytracing
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Computer Graphics and Image Processing)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9424-1272
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Scientific Visualization)ORCID iD: 0000-0002-5220-633X
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Show others and affiliations
2014 (English)In: Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), 2014, IEEE Signal Processing Society, 2014Conference paper, Published paper (Refereed)
Abstract [en]

The recent introduction of HDR video cameras has enabled the development of image based lighting techniques for rendering virtual objects illuminated with temporally varying real world illumination. A key challenge in this context is that rendering realistic objects illuminated with video environment maps is computationally demanding. In this work, we present a GPU based rendering system based on the NVIDIA OptiX framework, enabling real time raytracing of scenes illuminated with video environment maps. For this purpose, we explore and compare several Monte Carlo sampling approaches, including bidirectional importance sampling, multiple importance sampling and sequential Monte Carlo samplers. While previous work have focused on synthetic data and overly simple environment maps sequences, we have collected a set of real world dynamic environment map sequences using a state-of-art HDR video camera for evaluation and comparisons.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2014.
Keyword [en]
High dynamic range imaging, image synthesis, iamge based lighting
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-107638OAI: oai:DiVA.org:liu-107638DiVA: diva2:726212
Conference
22nd European Signal Processing Conference (EUSIPCO 2014), 1-5 September 2014, Lisbon, Portugal
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IISS-0081
Available from: 2014-06-17 Created: 2014-06-17 Last updated: 2016-05-04Bibliographically approved

Open Access in DiVA

fulltext(17711 kB)422 downloads
File information
File name FULLTEXT02.pdfFile size 17711 kBChecksum SHA-512
e3a112dd9674e1ab40e7ec08a16cc9b4aa9facdf45f75940fb86fd9706255de706016ca48804b2adefc63eef949c4b392d4a94af06f542cf2d4edef21083aa3a
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Kronander, JoelDahlin, JohanJönsson, DanielKok, ManonSchön, ThomasUnger, Jonas
By organisation
Media and Information TechnologyThe Institute of TechnologyAutomatic Control
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 422 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 498 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf