论文标题
3D点云上采样的特征扩展单元的比较研究
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling
论文作者
论文摘要
最近，深度学习方法在3D Point Cloud Up采样中取得了巨大的成功。在这些方法中，提出了许多特征扩展单元，以完成点的末尾扩展。在本文中，我们通过理论分析和定量实验比较了各种特征扩展单元。我们表明，大多数现有功能扩展单元都独立处理每个点功能，同时忽略了不同点之间的特征交互。此外，我们灵感来自图像超分辨率的UP采样模块以及点云上动态图CNN的最新成功，我们提出了一个名为ProdeDgeshle的新型特征扩展单元。实验表明，我们提出的方法可以比以前的特征扩展单元实现大量改进。
Recently, deep learning methods have shown great success in 3D point cloud upsampling. Among these methods, many feature expansion units were proposed to complete point expansion at the end. In this paper, we compare various feature expansion units by both theoretical analysis and quantitative experiments. We show that most of the existing feature expansion units process each point feature independently, while ignoring the feature interaction among different points. Further, inspired by upsampling module of image super-resolution and recent success of dynamic graph CNN on point clouds, we propose a novel feature expansion units named ProEdgeShuffle. Experiments show that our proposed method can achieve considerable improvement over previous feature expansion units.
