用于形状补全的自适应局部基函数
Adaptive Local Basis Functions for Shape Completion
AI总结：
针对深度隐函数点云补全中固定基函数成本高、表达力弱的问题，提出端到端学习的自适应局部基函数及局部到局部框架，以少量基函数保留细节并在多项指标上超越现有方法。
摘要：
本文聚焦于利用深度隐函数从局部点云进行三维形状补全的任务。现有方法倾向于使用体素化基函数或来自某一函数族（例如高斯函数）的基函数，这会导致高昂的计算成本或有限的形状表达能力。与之相反，我们的方法采用自适应局部基函数，这些基函数以端到端方式学习，且不受限于特定形式。基于这些基函数，本文提出了一种局部到局部的形状补全框架。我们的算法学习具有少量基函数的稀疏参数化，同时在补全过程中保留局部几何细节。定量与定性实验表明，我们的方法在形状补全、细节保留、对未见几何形状的泛化以及计算成本方面均优于最先进的方法。代码和数据位于 https://github.com/yinghdb/Adaptive-Local-Basis-Functions。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
