Adaptive Local Basis Functions for Shape Completion
Hui Ying Tianjia Shao He Wang Yin Yang Kun Zhou
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 https://github.com/yinghdb/Adaptive-Local-Basis-Functions.