  Among 2D convolutional networks on point clouds, point-based approaches
consume point clouds of fixed size directly. By analysis of PointNet, a pioneer
in introducing deep learning into point sets, we reveal that current
point-based methods are essentially spatial relationship processing networks.
In this paper, we take a different approach. Our architecture, named PE-Net,
learns the representation of point clouds in high-dimensional space, and
encodes the unordered input points to feature vectors, which standard 2D CNNs
can be applied to. The recommended network can adapt to changes in the number
of input points which is the limit of current methods. Experiments show that in
the tasks of classification and part segmentation, PE-Net achieves the
state-of-the-art performance in multiple challenging datasets, such as ModelNet
and ShapeNetPart.
