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arXiv 2203.06008cs.CG
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通过L1范数最小化实现光滑可定向子流形的类Delaunay三角剖分

Delaunay-like Triangulation of Smooth Orientable Submanifolds by L1-Norm Minimization

Dominique Attali, André Lieutier

AI总结:

针对欧氏空间中可定向光滑d维子流形的形状重建问题,提出将其转化为以单纯复形的d链为优化变量的L1范数最小化问题,证明解唯一且为类Delaunay三角剖分,可通过线性规划实现。

摘要:

本文研究形状重建问题,待重建的形状是欧氏空间中可定向的光滑d维子流形。假设输入为近似该子流形的单纯复形K(例如Cech复形或Rips复形),我们将从K重建子流形的问题重新表述为L1范数最小化问题,其中优化变量是K的一条d链。在K满足某些合理条件的前提下,我们证明所考虑的最小化问题存在唯一解,该解可对该子流形进行三角剖分,且与在配套论文中提出并研究的平坦Delaunay复形一致。由于目标函数是加权L1范数且约束为线性,因此该三角剖分过程可通过线性规划实现。

英文摘要:

In this paper, we study the shape reconstruction problem, when the shape we wish to reconstruct is an orientable smooth d-dimensional submanifold of the Euclidean space. Assuming we have as input a simplicial complex K that approximates the submanifold (such as the Cech complex or the Rips complex), we recast the problem of reconstucting the submanifold from K as a L1-norm minimization problem in which the optimization variable is a d-chain of K. Providing that K satisfies certain reasonable conditions, we prove that the considered minimization problem has a unique solution which triangulates the submanifold and coincides with the flat Delaunay complex introduced and studied in a companion paper. Since the objective is a weighted L1-norm and the constraints are linear, the triangulation process can thus be implemented by linear programming.

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。

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