  Point cloud patterns are hard to learn because of the implicit local geometry
features among the orderless points. In recent years, point cloud
representation in 2D space has attracted increasing research interest since it
exposes the local geometry features in a 2D space. By projecting those points
to a 2D feature map, the relationship between points is inherited in the
context between pixels, which are further extracted by a 2D convolutional
neural network. However, existing 2D representing methods are either accuracy
limited or time-consuming. In this paper, we propose a novel 2D representation
method that projects a point cloud onto an ellipsoid surface space, where local
patterns are well exposed in ellipsoid-level and point-level. Additionally, a
novel convolutional neural network named EllipsoidNet is proposed to utilize
those features for point cloud classification and segmentation applications.
The proposed methods are evaluated in ModelNet40 and ShapeNet benchmarks, where
the advantages are clearly shown over existing 2D representation methods.
