论文标题
pu-eva：基于边缘矢量的近似解决方案，用于灵活的点云上采样
PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling
论文作者
论文摘要
高质量的云对于基于点的渲染，语义理解和表面重建具有实际意义。提高稀疏，嘈杂和不均匀的点云，以使目标对象的更常规近似是一项可取但具有挑战性的任务。大多数现有方法复制了用于上采样的点特征，以固定的速率约束UPSMPLING量表。在这项工作中，通过基于边缘矢量的仿射组合来实现灵活的上采样率，并提出了基于边缘矢量的新型设计，用于柔性尺度点云云量采样（PU-EVA）。基于边缘矢量的近似值通过基于边缘向量的仿射组合编码相邻的连接性，并限制了泰勒（Taylor）扩展的二阶项内的近似误差。 EVA的提升采样将较高采样量表与网络体系结构取代，从而在一次性培训中实现了灵活的上采样率。定性和定量评估表明，拟议的PU-EVA在接近面，分布均匀性和几何细节保护方面优于最先进的。
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.
