Tensor displays

By Gordon Wetzstein, Douglas Lanman, Matthew Hirsch, Ramesh Raskar. ACM Transactions on Graphics, 2012

319 citations recorded by the source corpus.

We introduce tensor displays: a family of compressive light field displays comprising all architectures employing a stack of time-multiplexed, light-attenuating layers illuminated by uniform or directional backlighting (i.e., any low-resolution light field emitter). We show that the light field emitted by an N-layer, M-frame tensor display can be represented by an Nth-order, rank-M tensor. Using this representation we introduce a unified optimization framework, based on nonnegative tensor factorization (NTF), encompassing all tensor display architectures. This framework is the first to allow joint multilayer, multiframe light field decompositions, significantly reducing artifacts observed with prior multilayer-only and multiframe-only decompositions; it is also the first optimization method for designs combining multiple layers with directional backlighting. We verify the benefits and limitations of tensor displays by constructing a prototype using modified LCD panels and a custom integral imaging backlight. Our efficient, GPU-based NTF implementation enables interactive applications. Through simulations and experiments we show that tensor displays reveal practical architectures with greater depths of field, wider fields of view, and thinner form factors, compared to prior automultiscopic displays.

Subfields: Computational Mathematics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8209a593715ac249da6f91ae14757e989f349f39

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In Computational Mathematics

In Computer Vision and Pattern Recognition

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Citations in the corpus

Local light field fusion (2019, unknown): “An MPI can be considered as an encoding of a local light field, similar to layered light field displays [Wetzstein et al. 2011, 2012].”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, methodology): “Consequently, in signal processing and data analysis, CPD is typically used for factorizing data into easy to interpret components (i.e., the rank-1 terms), while the goal of unconstrained Tucker decompositions is most often to compress data into a tensor of smaller size (i.e., the core tensor) or…”