  Learning new representations of 3D point clouds is an active research area in
3D vision, as the order-invariant point cloud structure still presents
challenges to the design of neural network architectures. Recent works explored
learning either global or local features or both for point clouds, however none
of the earlier methods focused on capturing contextual shape information by
analysing local orientation distribution of points. In this paper, we leverage
on point orientation distributions around a point in order to obtain an
expressive local neighborhood representation for point clouds. We achieve this
by dividing the spherical neighborhood of a given point into predefined cone
volumes, and statistics inside each volume are used as point features. In this
way, a local patch can be represented by not only the selected point's nearest
neighbors, but also considering a point density distribution defined along
multiple orientations around the point. We are then able to construct an
orientation distribution function (ODF) neural network that involves an
ODFBlock which relies on mlp (multi-layer perceptron) layers. The new ODFNet
model achieves state-of the-art accuracy for object classification on
ModelNet40 and ScanObjectNN datasets, and segmentation on ShapeNet S3DIS
datasets.
