  Recent state-of-the-art methods for point cloud processing are based on the
notion of point convolution, for which several approaches have been proposed.
In this paper, inspired by discrete convolution in image processing, we provide
a formulation to relate and analyze a number of point convolution methods. We
also propose our own convolution variant, that separates the estimation of
geometry-less kernel weights and their alignment to the spatial support of
features. Additionally, we define a point sampling strategy for convolution
that is both effective and fast. Finally, using our convolution and sampling
strategy, we show competitive results on classification and semantic
segmentation benchmarks while being time and memory efficient.
