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Multiway K-Clustered Tensor Approximation: Toward High-Performance Photorealistic Data-Driven Rendering
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DOI：10.1145/2753756.png)
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Abstract
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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.
Keywords:
Algorithms
Real-time rendering
multidimensional data analysis
tensor approximation
multiway clustering
sparse representation
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