HMK-CTA: A Hierarchical Multidimensional Representation for Visual Datasets
Article No.: 21, Pages 1 - 10
Abstract
This paper presents a novel tensor-based representation, namely hierarchical multiway K-clustered tensor approximation, for multidimensional visual datasets. The proposed method extends a previous tensor model [20] into a hierarchical representation that can significantly reduce offline computational cost as well as provide similar approximation quality and rendering performance at the same time. We also apply the proposed method to approximate spatially-varying bidirectional reflectance distribution functions, time-varying light fields, and time-varying volume data to show its effectiveness and potential for data-driven rendering. Experimental results demonstrate that under similar performance to previous work, the proposed method can reduce offline approximation time by even an order of magnitude.
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- HMK-CTA: A Hierarchical Multidimensional Representation for Visual Datasets
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Published In
June 2024
437 pages
ISBN:9798400718281
DOI:10.1145/3670947
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Published: 21 September 2024
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GI '24
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