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Found 5 papers, 3 papers with code
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes
ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.
A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks.
A fundamental problem in equivariant deep learning is to design activation functions which are both informative and preserve equivariance.
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.
Effective Rotation-invariant Point CNN with Spherical Harmonics kernels
We present a novel rotation invariant architecture operating directly on point cloud data.
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Our construction, which we call multi-directional geodesic convolution, or directional convolution for short, allows, in particular, to propagate and relate directional information across layers and thus different regions on the shape.
