NDLI: Multiway K-Clustered Tensor Approximation: Toward High-Performance Photorealistic Data-Driven Rendering

Content Provider	ACM Digital Library
Author	Tsai, Yu-Ting
Copyright Year	2015
Abstract	This article presents a generalized sparse multilinear model, namely multiway K-clustered tensor approximation (MK-CTA), for synthesizing photorealistic 3D images from large-scale multidimensional visual datasets. MK-CTA extends previous tensor approximation algorithms, particularly K-clustered tensor approximation (K-CTA) [Tsai and Shih 2012], to partition a multidimensional dataset along more than one dimension into overlapped clusters. On the contrary, K-CTA only sparsely clusters a dataset along just one dimension and often fails to efficiently approximate other unclustered dimensions. By generalizing K-CTA with multiway sparse clustering, MK-CTA can be regarded as a novel sparse tensor-based model that simultaneously exploits the intra- and inter-cluster coherence among different dimensions of an input dataset. Our experiments demonstrate that MK-CTA can accurately and compactly represent various multidimensional datasets with complex and sharp visual features, including bidirectional texture functions (BTFs) [Dana et al. 1999], time-varying light fields (TVLFs) [Bando et al. 2013], and time-varying volume data (TVVD) [Wang et al. 2010], while easily achieving high rendering rates in practical graphics applications.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	07300301
e-ISSN	15577368
DOI	10.1145/2753756
Volume Number	34
Issue Number	5
Journal	ACM Transactions on Graphics (TOG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Real-time rendering Multidimensional data analysis Multiway clustering Sparse representation Tensor approximation
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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