ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes. (arXiv:2201.07788v1 [cs.CV])

Progress in 3D object understanding has relied on manually canonicalized
shape datasets that contain instances with consistent position and orientation
(3D pose). This has made it hard to generalize these methods to in-the-wild
shapes, eg., from internet model collections or depth sensors. ConDor is a
self-supervised method that learns to Canonicalize the 3D orientation and
position for full and partial 3D point clouds. We build on top of Tensor Field
Networks (TFNs), a class of permutation- and rotation-equivariant, and
translation-invariant …

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