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.

通过对点卷积操作的不同嵌入机制实验研究，我们提供了第一项广泛的研究，旨在分析这些嵌入机制如何单独编码点邻域信息，并给出了一些建议，可以帮助改进点云的神经网络架构设计。我们发现，最常用的基于多层感知器（MLP）和 ReLU 激活函数的嵌入机制在所有嵌入方式中表现最差，甚至在某些任务上被简单的点坐标线性组合方式超越。此外，我们展示了基于这些嵌入机制的简单卷积神经网络在多个任务上取得了最先进的结果，优于最近和更复杂的操作。最后，我们展示这些发现可以推广到其他更复杂的卷积操作，通过遵循我们的建议，能够改进最近的最先进架构设计。