  The recent advances in 3D sensing technology have made possible the capture
of point clouds in significantly high resolution. However, increased detail
usually comes at the expense of high storage, as well as computational costs in
terms of processing and visualization operations. Mesh and Point Cloud
simplification methods aim to reduce the complexity of 3D models while
retaining visual quality and relevant salient features. Traditional
simplification techniques usually rely on solving a time-consuming optimization
problem, hence they are impractical for large-scale datasets. In an attempt to
alleviate this computational burden, we propose a fast point cloud
simplification method by learning to sample salient points. The proposed method
relies on a graph neural network architecture trained to select an arbitrary,
user-defined, number of points from the input space and to re-arrange their
positions so as to minimize the visual perception error. The approach is
extensively evaluated on various datasets using several perceptual metrics.
Importantly, our method is able to generalize to out-of-distribution shapes,
hence demonstrating zero-shot capabilities.
