Adaptive Local Basis Functions for Shape Completion

TL;DR

This paper introduces an adaptive local basis function approach for 3D shape completion from partial point clouds, which improves detail preservation and reduces computational costs compared to existing methods.

Contribution

The paper proposes a novel end-to-end learnable adaptive local basis function framework that enhances shape expressivity and efficiency in 3D shape completion.

Findings

Outperforms state-of-the-art in shape completion accuracy

Preserves local geometric details effectively

Reduces computational costs significantly

Abstract

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.