In this paper, we focus on the task of 3D shape completion from partial point
clouds using deep implicit functions. Existing methods seek to use voxelized
basis functions or the ones from a certain family of functions (e.g.,
Gaussians), which leads to high computational costs or limited shape
expressivity. On the contrary, our method employs adaptive local basis
functions, which are learned end-to-end and not restricted in certain forms.
Based on those basis functions, a local-to-local shape completion framework is
presented. Our algorithm learns sparse parameterization with a small number of
basis functions while preserving local geometric details during completion.
Quantitative and qualitative experiments demonstrate that our method
outperforms the state-of-the-art methods in shape completion, detail
preservation, generalization to unseen geometries, and computational cost. Code
and data are at this https URL

本文针对使用深度隐式函数从部分点云中完成 3D 形状的任务进行研究，利用自适应本地基函数，提出了一种局部对局部形状完成框架，该算法能够在完成过程中保留局部几何细节并且比现有方法都有着更好的表现，同时也具有更好的泛化能力和性能。