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Learning Based Compression of Surface Light Fields for Real-time Rendering of Global Illumination Scenes
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 Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Computer Graphics and Image Processing)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (Computer Graphics and Image Processing)ORCID iD: 0000-0002-7765-1747
2013 (English)In: Proceedings of ACM SIGGRAPH ASIA 2013, ACM Press, 2013Conference paper, Published paper (Refereed)
Abstract [en]

We present an algorithm for compression and real-time rendering of surface light fields (SLF) encoding the visual appearance of objects in static scenes with high frequency variations. We apply a non-local clustering in order to exploit spatial coherence in the SLFdata. To efficiently encode the data in each cluster, we introducea learning based approach, Clustered Exemplar Orthogonal Bases(CEOB), which trains a compact dictionary of orthogonal basispairs, enabling efficient sparse projection of the SLF data. In ad-dition, we discuss the application of the traditional Clustered Principal Component Analysis (CPCA) on SLF data, and show that inmost cases, CEOB outperforms CPCA, K-SVD and spherical harmonics in terms of memory footprint, rendering performance andreconstruction quality. Our method enables efficient reconstructionand real-time rendering of scenes with complex materials and lightsources, not possible to render in real-time using previous methods.

Place, publisher, year, edition, pages
ACM Press, 2013.
Keywords [en]
computer graphics, global illumination, real-time, machine learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-99433DOI: 10.1145/2542355.2542385ISBN: 978-1-4503-2629-2 (print)OAI: oai:DiVA.org:liu-99433DiVA, id: diva2:657087
Conference
SIGGRAPH Asia, 19-22 November 2013, Hong Kong
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IIS11-0081Swedish Research CouncilAvailable from: 2013-10-17 Created: 2013-10-17 Last updated: 2018-11-23Bibliographically approved
In thesis
1. Sparse representation of visual data for compression and compressed sensing
Open this publication in new window or tab >>Sparse representation of visual data for compression and compressed sensing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications.

Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time.

This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 158
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1963
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-152863 (URN)10.3384/diss.diva-152863 (DOI)9789176851869 (ISBN)
Public defence
2018-12-14, Domteatern, Visualiseringscenter C, Kungsgatan 54, Campus Norrköping, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2018-11-23Bibliographically approved

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Miandji, EhsanKronander, JoelUnger, Jonas

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