  Point cloud sampling is a less explored research topic for this data
representation. The most commonly used sampling methods are still classical
random sampling and farthest point sampling. With the development of neural
networks, various methods have been proposed to sample point clouds in a
task-based learning manner. However, these methods are mostly generative-based,
rather than selecting points directly using mathematical statistics. Inspired
by the Canny edge detection algorithm for images and with the help of the
attention mechanism, this paper proposes a non-generative Attention-based Point
cloud Edge Sampling method (APES), which captures salient points in the point
cloud outline. Both qualitative and quantitative experimental results show the
superior performance of our sampling method on common benchmark tasks.
