  Point cloud upsampling aims to generate dense point clouds from given sparse
ones, which is a challenging task due to the irregular and unordered nature of
point sets. To address this issue, we present a novel deep learning-based
model, called PU-Flow, which incorporates normalizing flows and weight
prediction techniques to produce dense points uniformly distributed on the
underlying surface. Specifically, we exploit the invertible characteristics of
normalizing flows to transform points between Euclidean and latent spaces and
formulate the upsampling process as ensemble of neighbouring points in a latent
space, where the ensemble weights are adaptively learned from local geometric
context. Extensive experiments show that our method is competitive and, in most
test cases, it outperforms state-of-the-art methods in terms of reconstruction
quality, proximity-to-surface accuracy, and computation efficiency. The source
code will be publicly available at https://github.com/unknownue/pu-flow.
