Recent advances in deep learning for 3D point clouds have shown great
promises in scene understanding tasks thanks to the introduction of convolution
operators to consume 3D point clouds directly in a neural network. Point cloud
data, however, could have arbitrary rotations, especially those acquired from
3D scanning. Recent works show that it is possible to design point cloud
convolutions with rotation invariance property, but such methods generally do
not perform as well as translation-invariant only convolution. We found that a
key reason is that compared to point coordinates, rotation-invariant features
consumed by point cloud convolution are not as distinctive. To address this
problem, we propose a novel convolution operator that enhances feature
distinction by integrating global context information from the input point
cloud to the convolution. To this end, a globally weighted local reference
frame is constructed in each point neighborhood in which the local point set is
decomposed into bins. Anchor points are generated in each bin to represent
global shape features. A convolution can then be performed to transform the
points and anchor features into final rotation-invariant features. We conduct
several experiments on point cloud classification, part segmentation, shape
retrieval, and normals estimation to evaluate our convolution, which achieves
state-of-the-art accuracy under challenging rotations
