3D object classification and retrieval with Spherical CNNs

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Set2Graph: Learning Graphs From Sets (2020, background): “…images with rotational and reﬂectional symmetries (Cohen & Welling, 2016a;b; Dieleman et al., 2016; Worrall et al., 2017), learning functions on spheres (Cohen et al., 2018; Esteves et al., 2017) and learning general 3D data (Weiler et al., 2018; Worrall & Brostow, 2018; Weiler et al., 2018).”
Cormorant: Covariant Molecular Neural Networks (2019, methodology): “…of using “Fourier space” activations, i.e., expressing the activations of neurons in a basis deﬁned by the irreducible representations of the underlying symmetry group (see also [Esteves et al., 2017]), and these ideas were later generalized to the entire SE (3) group [Weiler et al., 2018].”
A General Theory of Equivariant CNNs on Homogeneous Spaces (2018, positive): “Indeed, if ρ(h) = 1 is the trivial representation, we see that←− κ is constant on these orbits, in agreement with [42] who use isotropic filters.”
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation (2018, background): “Spherical CNN [5, 7] projects 3D shapes onto spheres and process the signal with spherical ﬁlters for rotation equivariant representation.”
On Learning Sets of Symmetric Elements (2020, background): “, 2017), spherical symmetries (Cohen et al., 2018; 2019b; Esteves et al., 2017), or 3D symmetries (Weiler et al.”