  High-quality point clouds have practical significance for point-based
rendering, semantic understanding, and surface reconstruction. Upsampling
sparse, noisy and nonuniform point clouds for a denser and more regular
approximation of target objects is a desirable but challenging task. Most
existing methods duplicate point features for upsampling, constraining the
upsampling scales at a fixed rate. In this work, the flexible upsampling rates
are achieved via edge vector based affine combinations, and a novel design of
Edge Vector based Approximation for Flexible-scale Point clouds Upsampling
(PU-EVA) is proposed. The edge vector based approximation encodes the
neighboring connectivity via affine combinations based on edge vectors, and
restricts the approximation error within the second-order term of Taylor's
Expansion. The EVA upsampling decouples the upsampling scales with network
architecture, achieving the flexible upsampling rates in one-time training.
Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA
outperforms the state-of-the-art in terms of proximity-to-surface, distribution
uniformity, and geometric details preservation.
