Abstract 3D point clouds deep learning is a promising field of research that allows a neural
network to learn features of point clouds directly, making it a robust tool for solving 3D scene …

Despite the progress on 3D point cloud deep learning, most prior works focus on learning
features that are invariant to translation and point permutation, and very limited efforts have …

This study proposes a set of generic rules to revise existing neural networks for 3D point
cloud processing to rotation-equivariant quaternion neural networks (REQNNs), in order to …

Equivariance of neural networks to transformations helps to improve their performance and
reduce generalization error in computer vision tasks, as they apply to datasets presenting …

Invariance under symmetry is an important problem in machine learning. Our paper looks
specifically at equivariant neural networks where transformations of inputs yield …

Recent investigations on rotation invariance for 3D point clouds have been devoted to
devising rotation-invariant feature descriptors or learning canonical spaces where objects …

Real-world point clouds usually suffer from incompleteness and display different poses.
While current point cloud completion methods excel in reproducing complete point clouds …

While object semantic understanding is essential for service robotic tasks, 3D object
classification is still an open problem. Learning from artificial 3D models alleviates the cost …

While object semantic understanding is essential for most service robotic tasks, 3D object
classification is still an open problem. Learning from artificial 3D models alleviates the cost …

Point cloud is a crucial data format for 3D vision, but its irregularity makes it challenging to
comprehend the associated geometric information. Although some previous research has …