K-clustered tensor approximation

Abstract

With the increasing demands for photo-realistic image synthesis in real time, we propose a sparse multilinear model, which is named K-Clustered Tensor Approximation (K-CTA), to efficiently analyze and approximate large-scale multidimensional visual datasets, so that both storage space and rendering time are substantially reduced. K-CTA not only extends previous work on Clustered Tensor Approximation (CTA) to exploit inter-cluster coherence, but also allows a compact and sparse representation for high-dimensional datasets with just a few low-order factors and reduced multidimensional cluster core tensors. Thus, K-CTA can be regarded as a sparse extension of CTA and a multilinear generalization of sparse representation. Experimental results demonstrate that K-CTA can accurately approximate spatially varying visual datasets, such as bidirectional texture functions, view-dependent occlusion texture functions, and biscale radiance transfer functions for efficient rendering in real-time applications.