3D point cloud completion, the task of inferring the complete geometric shape
from a partial point cloud, has been attracting attention in the community. For
acquiring high-fidelity dense point clouds and avoiding uneven distribution,
blurred details, or structural loss of existing methods' results, we propose a
novel approach to complete the partial point cloud in two stages. Specifically,
in the first stage, the approach predicts a complete but coarse-grained point
cloud with a collection of parametric surface elements. Then, in the second
stage, it merges the coarse-grained prediction with the input point cloud by a
novel sampling algorithm. Our method utilizes a joint loss function to guide
the distribution of the points. Extensive experiments verify the effectiveness
of our method and demonstrate that it outperforms the existing methods in both
the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

本文提出了一种新的方法，通过两个阶段，利用参数化表面元素和采样算法预测粗粒度点云并与输入点云合并来完成部分点云。实验结果表明，该方法在 Earth Mover's Distance（EMD）和 Chamfer Distance（CD）等方面表现优异，优于现有方法。

稠密点云补全的变形采样网络

Morphing and Sampling Network for Dense Point Cloud Completion

We introduce a method for learning to generate the surface of 3D shapes. Our
approach represents a 3D shape as a collection of parametric surface elements
and, in contrast to methods generating voxel grids or point clouds, naturally
infers a surface representation of the shape. Beyond its novelty, our new shape
generation framework, AtlasNet, comes with significant advantages, such as
improved precision and generalization capabilities, and the possibility to
generate a shape of arbitrary resolution without memory issues. We demonstrate
these benefits and compare to strong baselines on the ShapeNet benchmark for
two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction
from a still image. We also provide results showing its potential for other
applications, such as morphing, parametrization, super-resolution, matching,
and co-segmentation.

该研究介绍了一种生成 3D 形状表面的方法 ——AtlasNet，它将 3D 形状表示为参数化表面元素的集合，相较于生成体素网格或点云的方法，具有更好的精度和泛化能力，并可以生成任意分辨率的形状。该方法在自编码形状和单视图重建方面取得了比较优异的结果，并且在变形、参数化、超分辨率、匹配和共同分割等方面显示其潜力。