  Deep neural networks require specific layers to process point clouds, as the
scattered and irregular location of points prevents us from using convolutional
filters. Here we introduce the composite layer, a new convolutional operator
for point clouds. The peculiarity of our composite layer is that it extracts
and compresses the spatial information from the position of points before
combining it with their feature vectors. Compared to well-known
point-convolutional layers such as those of ConvPoint and KPConv, our composite
layer provides additional regularization and guarantees greater flexibility in
terms of design and number of parameters. To demonstrate the design
flexibility, we also define an aggregate composite layer that combines spatial
information and features in a nonlinear manner, and we use these layers to
implement a convolutional and an aggregate CompositeNet. We train our
CompositeNets to perform classification and, most remarkably, unsupervised
anomaly detection. Our experiments on synthetic and real-world datasets show
that, in both tasks, our CompositeNets outperform ConvPoint and achieve similar
results as KPConv despite having a much simpler architecture. Moreover, our
CompositeNets substantially outperform existing solutions for anomaly detection
on point clouds.
