  Point convolution operations rely on different embedding mechanisms to encode
the neighborhood information of each point in order to detect patterns in 3D
space. However, as convolutions are usually evaluated as a whole, not much work
has been done to investigate which is the ideal mechanism to encode such
neighborhood information. In this paper, we provide the first extensive study
that analyzes such Point Neighborhood Embeddings (PNE) alone in a controlled
experimental setup. From our experiments, we derive a set of recommendations
for PNE that can help to improve future designs of neural network architectures
for point clouds. Our most surprising finding shows that the most commonly used
embedding based on a Multi-layer Perceptron (MLP) with ReLU activation
functions provides the lowest performance among all embeddings, even being
surpassed on some tasks by a simple linear combination of the point
coordinates. Additionally, we show that a neural network architecture using
simple convolutions based on such embeddings is able to achieve
state-of-the-art results on several tasks, outperforming recent and more
complex operations. Lastly, we show that these findings extrapolate to other
more complex convolution operations, where we show how following our
recommendations we are able to improve recent state-of-the-art architectures.
