Our products are based on proprietary in-house research. We run a cutting-edge, multi-disciplinary research team in AI and VR that not only develops the technologies underlying our products, but also performs fundamental research in these areas.

We believe it is our unique multidisciplinary approach, drawing on our backgrounds in physics, computer graphics and vision, applied mathematics, and machine learning, that allows us to bring new insights to solve open problems across these fields.

Efficient Generalized Spherical CNNs

Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d’Avezac and Jason D. McEwen

Abstract: Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The only existing non-linear spherical CNN layer that is strictly equivariant has complexity O(C2L5), where C is a measure of representational capacity and L the spherical harmonic bandlimit. Such a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity O(CL4) and O(CL3 log L), making larger, more expressive models computationally feasible. Moreover, we adopt efficient sampling theory to achieve further computational savings. We show that these developments allow the construction of more expressive hybrid models that achieve state-of-the-art accuracy and parameter efficiency on spherical benchmark problems.

Open Source

We make use of and contribute to a number of open source codes developed initially by Kagenova team members.

  • SSHT: Spin spherical harmonic transforms
    Functionality to perform fast, exact spin spherical harmonic transforms based on the efficient sampling theory on the sphere of McEwen & Wiaux (2011).

  • SO3: Fast Wigner transforms on the rotation group
    Functionality to compute fast, exact Wigner transforms on the rotation group based on the efficient sampling theory of McEwen et al. (2015).


The technology behind our products is patented in a number of juristictions, with further jurisdictions and patents pending.

  • Method and system for providing at least a portion of content having six degrees of freedom motion

Related Research

Beyond our work at Kagenova, several team members have done important research in related fields. A selection of articles related to the analysis of 360° spherical data is included below.

Further publications can be found here.