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.

翻译：高质量的点云对于点基成像、 语义理解和表面重建具有实际意义。 为更稠密、 更经常接近目标对象的云层取样稀疏、 吵闹和非统一点云层是一项可取但具有挑战性的任务。 多数现有方法重复了点样取样的特征, 以固定速率限制升级的标度尺度。 在这项工作中, 灵活的加压率是通过边向矢量组合实现的, 在一次性培训中, 提出了基于电动矢量的弹性矢量加压模拟点云( PU- EVA ) 的新设计。 以近距离值为基础的边缘矢量根据边缘矢量组合编码相邻连接的连接, 并限制泰勒扩张第二阶期内的近点误差。 EVA 将加压率比对网络结构的加压标度, 在一次性培训中实现灵活的加压率加压率。 定性和定量评价显示, 拟议的PU- EVA 显示, 以边缘矢量值组合为基础、 度分布的地平面上, 和精确度分布。