Approximating the Riemannian Metric from Point Clouds via Manifold Moving Least Squares

Abstract

The approximation of both geodesic distances and shortest paths on point cloud sampled from an embedded submanifold $\mathcal{M}$ of Euclidean space has been a long-standing challenge in computational geometry. Given a sampling resolution parameter $h$, state-of-the-art discrete methods yield $O(h)$ provable approximations. In this paper, we investigate the convergence of such approximations made by Manifold Moving Least-Squares (Manifold-MLS), a method that constructs an approximating manifold ${\mathcal{M}}^h$ using information from a given point cloud that was developed by Sober \& Levin in 2019. In this paper, we show that provided that $\mathcal{M}\in C^k$ and closed (i.e. $\mathcal{M}$ is a compact manifold without boundary) the Riemannian metric of ${\mathcal{M}}^h$ approximates the Riemannian metric of $\mathcal{M},$. Explicitly, given points $p_1,p_2\in \mathcal{M}$ with geodesic distance ${\rho }_{\mathcal{M}}(p_1,p_2)$, we show that their corresponding points $p_1^h,p_2^h\in {\mathcal{M}}^h$ have a geodesic distance of ${\rho }_{{\mathcal{M}}^h}(p_1^h,p_2^h)={\rho }_{\mathcal{M}}(p_1,p_2)(1+O(h^{k-1}))$ (i.e., the Manifold-MLS is nearly an isometry). We then use this result, as well as the fact that ${\mathcal{M}}^h$ can be sampled with any desired resolution, to devise a naive algorithm that yields approximate geodesic distances with a rate of convergence $O(h^{k-1})$. We show the potential and the robustness to noise of the proposed method on some numerical simulations.