Cech Persistence diagrams (PDs) are topological descriptors routinely used to capture the geometry of complex datasets. They are commonly compared using the Wasserstein distances $\mathrm{OT}_p$; however, the extent to which PDs are stable with respect to these metrics remains poorly understood. We partially close this gap by focusing on the case where datasets are sampled on an $m$-dimensional submanifold of $\mathbb{R}^d$. Under this manifold hypothesis, we show that convergence with respect to the $\mathrm{OT}_p$ metric happens exactly when $p>m$. We also provide improvements upon the bottleneck stability theorem in this case and prove new laws of large numbers for the total $\alpha$-persistence of PDs. Finally, we show how these theoretical findings shed new light on the behavior of the feature maps on the space of PDs that are used in ML-oriented applications of Topological Data Analysis.

We introduce a total order on n -simplices in the n -Euclidean space for which the support of the lexicographic-minimal chain with the convex hull boundary as boundary constraint is precisely the n -dimensional Delaunay triangulation, or in a more general setting, the regular triangulation of a set of weighted points. This new characterization of regular and Delaunay triangulations is motivated by its possible generalization to submanifold triangulations as well as the recent development of polynomial-time triangulation algorithms taking advantage of this order.

In general, the critical points of the distance function d_𝖬 to a compact submanifold 𝖬⊂ℝ^D can be poorly behaved. In this article, we show that this is generically not the case by listing regularity conditions on the critical and μ-critical points of a submanifold and by proving that they are generically satisfied and stable with respect to small C^2 perturbations. More specifically, for any compact abstract manifold M, the set of embeddings i:M→ℝ^D such that the submanifold i(M) satisfies those conditions is open and dense in the Whitney C^2-topology. When those regularity conditions are fulfilled, we prove that the critical points of the distance function to an ε-dense subset of the submanifold (e.g. obtained via some sampling process) are well-behaved. We also provide many examples that showcase how the absence of these conditions can result in pathological cases.

Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the input points enriched with quadric error metrics, where the generator of each cluster is the optimal 3D point for the sum of its quadric error metrics. This approach favors the placement of generators on sharp features, and tends to equidistribute the error among clusters. We reconstruct the output surface mesh from the adjacency between clusters and a constrained binary solver. We combine our clustering process with an adaptive refinement driven by the error. Compared to prior art, our method avoids dense reconstruction prior to simplification and produces immediately an optimized mesh.

This paper considers a particular case of the Optimal Homologous Chain Problem (OHCP) for integer modulo 2 coefficients, where optimality is meant as a minimal lexicographic order on chains induced by a total order on simplices. The matrix reduction algorithm used for persistent homology is used to derive polynomial algorithms solving this problem instance, whereas OHCP is NP-hard in the classical setting. The complexity is further improved to a quasilinear algorithm by leveraging a dual graph minimum cut formulation when the simplicial complex is a pseudomanifold. We then show how this particular instance of the problem is relevant, by providing an application in the context of point cloud triangulation.

Given an input 3D geometry such as a triangle soup or a point set, we address the problem of generating a watertight and orientable surface triangle mesh that strictly encloses the input. The output mesh is obtained by greedily refining and carving a 3D Delaunay triangulation on an offset surface of the input, while carving with empty balls of radius alpha. The proposed algorithm is controlled via two user-defined parameters: alpha and offset. Alpha controls the size of cavities or holes that cannot be traversed during carving, while offset controls the distance between the vertices of the output mesh and the input. Our algorithm is guaranteed to terminate and to yield a valid and strictly enclosing mesh, even for defect-laden inputs. Genericity is achieved using an abstract interface probing the input, enabling any geometry to be used, provided a few basic geometric queries can be answered. We benchmark the algorithm on large public datasets such as Thingi10k, and compare it to state-of-the-art approaches in terms of robustness, approximation, output complexity, speed, and peak memory consumption. Our implementation is available through the CGAL library.

In this paper, we prove a variant of the Burger-Brooks transfer principle which, combined with recent eigenvalue bounds for surfaces, allows to obtain upper bounds on the eigenvalues of graphs as a function of their genus. More precisely, we show the existence of a universal constants $C$ such that the $k$-th eigenvalue $\lambda_k^{nr}$ of the normalized Laplacian of a graph $G$ of (geometric) genus $g$ on $n$ vertices satisfies $$\lambda_k^{nr}(G) \leq C \frac{d_{\max}(g+k)}{n},$$ where $d_{\max}$ denotes the maximum valence of vertices of the graph. This result is tight up to a change in the value of the constant $C$, and improves recent results of Kelner, Lee, Price and Teng on bounded genus graphs. To show that the transfer theorem might be of independent interest, we relate eigenvalues of the Laplacian on a metric graph to the eigenvalues of its simple graph models, and discuss an application to the mesh partitioning problem, extending pioneering results of Miller-Teng-Thurston-Vavasis and Spielman-Tang to arbitrary meshes.

The spectrum of a network or graph $G=(V,E)$ with adjacency matrix A , consists of the eigenvalues of the normalized Laplacian $L= I - D^-1/2 A D^-1/2 $. This set of eigenvalues encapsulates many aspects of the structure of the graph, including the extent to which the graph posses community structures at multiple scales. We study the problem of approximating the spectrum, $łambda = (łambda_1,\dots,łambda_|V| )$, of G in the regime where the graph is too large to explicitly calculate the spectrum. We present a sublinear time algorithm that, given the ability to query a random node in the graph and select a random neighbor of a given node, computes a succinct representation of an approximation $\widetilde łambda = (\widetilde łambda_1,\dots,\widetilde łambda_|V| )$, such that $\|\widetilde łambda - łambda\|_1 łe ε |V|$. Our algorithm has query complexity and running time $exp(O(1/\eps))$, which is independent of the size of the graph, $|V|$. We demonstrate the practical viability of our algorithm on synthetically generated graphs, and on 15 different real-world graphs from the Stanford Large Network Dataset Collection, including social networks, academic collaboration graphs, and road networks. For the smallest of these graphs, we are able to validate the accuracy of our algorithm by explicitly calculating the true spectrum; for the larger graphs, such a calculation is computationally prohibitive. The spectra of these real-world networks reveal insights into the structural similarities and differences between them, illustrating the potential value of our algorithm for efficiently approximating the spectrum of large large networks.

We introduce an efficient computational method for generating dense and low distortion maps between two arbitrary surfaces of same genus. Instead of relying on semantic correspondences or surface parameterization, we directly optimize a variance-minimizing transport plan between two input surfaces that defines an as-conformal-as-possible inter-surface map satisfying a user-prescribed bound on area distortion. The transport plan is computed via two alternating convex optimizations, and is shown to minimize a generalized Dirichlet energy of both the map and its inverse. Computational efficiency is achieved through a coarse-to-fine approach in diffusion geometry, with Sinkhorn iterations modified to enforce bounded area distortion. The resulting inter-surface mapping algorithm applies to arbitrary shapes robustly, with little to no user interaction.

Geometric inference deals with the problem of recovering the geometry and topology of a compact subset K of R-d from an approximation by a finite set P. This problem has seen several important developments in the previous decade. Many of the proposed constructions share a common feature: they estimate the geometry of the underlying compact set K using offsets of P, that is r-sublevel set of the distance function to P. These offset correspond to what is called tubular neighborhoods in differential geometry. First and second-order geometric quantities are encoded in the tube K-r around a manifold. For instance, the classical tube formula asserts that it is possible to estimate the curvature of a compact smooth submanifold K from the volume of its offsets. One can hope that if the finite set P is close to K in the Hausdorff sense, some of this geometric information remains in the offsets of P. In this chapter, we will see how this idea can be used to infer generalized notions of curvature such as Federer's curvature measures.

Procede de traitement de donnees geometriques, comprenant les etapes suivantes : A. recevoir un volume de tolerance Ω, relatif a des donnees geometriques brutes de depart, B. initier une triangulation canonique dans le volume de tolerance, a partir d'un jeu S de points echantillons pris aux limites de ce volume de tolerance, et de points situes a l'exterieur de celui-ci, C. raffiner cette triangulation canonique, jusqu'a classifier les points de l'echantillon, ce qui fournit un maillage dense du volume de tolerance, et D. simplifier ce maillage dense par des operations de modification de triangulation, en preservant la topologie et la classification des points de l'echantillon.

We describe a framework for robust shape reconstruction from raw point sets, based on optimal transportation between measures, where the input point sets are seen as distribution of masses. In addition to robustness to defect-laden point sets, hampered with noise and outliers, our approach can reconstruct smooth closed shapes as well as piecewise smooth shapes with boundaries.

We propose a noise-adaptive shape reconstruction method specialized to smooth, closed shapes. Our algorithm takes as input a defect-laden point set with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that the inferred shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points computed in previous step.

Notre approche consiste a considerer le nuage de points en entree comme une mesure discrete (une distribution de masses), et a construire une approximation par une mesure continue (et constante par morceaux)sur les faces d’un complexe simplicial. La distance entre les deux mesures est calculee par une approximation du transport optimal obtenue par programmation lineaire, et le complexe simplicial est obtenu par decimation et optimisation d’une triangulation de Delaunay initialisee avec un sous-ensemble des points en entree. La distance utilisee est robuste a la fois au bruit et aux donnees aberrantes, et preserve les aretes vives et les bords des formes a reconstruire. Cette distance peut egalement servir comme outil de post-traitement sur des surfaces lisses reconstruites avec des methodes par fonction implicite.

We introduce a robust and feature-capturing surface reconstruction and simplification method that turns an input point set into a low triangle-count simplicial complex. Our approach starts with a (possibly non-manifold) simplicial complex filtered from a 3D Delaunay triangulation of the input points. This initial approximation is iteratively simplified based on an error metric that measures, through optimal transport, the distance between the input points and the current simplicial complex--both seen as mass distributions. Our approach is shown to exhibit both robustness to noise and outliers, as well as preservation of sharp features and boundaries. Our new feature-sensitive metric between point sets and triangle meshes can also be used as a post-processing tool that, from the smooth output of a reconstruction method, recovers sharp features and boundaries present in the initial point set.

We propose a robust, feature-preserving surface reconstruction algorithm which turns a point set with noise and outliers into a low triangle-count simplicial complex. Our approach starts with a simplicial complex filtered from a 3D Delaunay triangulation of the input points. This initial approximation is iteratively simplified based on the optimal cost to transport the point set to the simplicial complex, both seen as measures (or mass distributions). Our optimal transport formulation allows the recovery of sharp features even in the presence of a large amount of outliers and/or noise in the input set. Key-words: Optimal transportation, Wasserstein distance, Linear programming, Surface reconstruction, Shape simplification, Feature recovery. ∗ GEOMETRICA INRIA Sophia Antipolis † California Institute of Technology ha l-0 07 06 71 2, v er si on 1 11 J un 2 01 2 Reconstruction et simplification de surfaces à partir de nuages de points imparfaits Résumé : Nous proposons une méthode robuste de reconstruction de surface qui préserve les bords et les arêtes vives. Cette méthode part d’un nuage de points bruités et contenant des points aberrants pour reconstruire un complexe simplicial parcimonieux. Notre approche débute par la construction d’un complexe simplicial par filtrage d’une triangulation de Delaunay des points initiaux. Cette approximation initiale est ensuite itérativement simplifiée en se basant sur le coût de transport entre le nuage de points et le complexe simplicial, ceux-ci étant vus comme des distributions de masse. Cette formulation basée sur le transport optimal entre les deux distributions permet de retrouver les arêtes vives même en présence de nombreux points aberrants ou de bruit dans le nuage de points initial. Mots-clés : Transport optimal, Distance de Wasserstein, Programmation linéaire, Reconstruction de surface, Simplification de forme, Recouvrement d’arête et de bord ha l-0 07 06 71 2, v er si on 1 11 J un 2 01 2 Feature-Preserving Surface Reconstruction and Simplification from Defect-Laden Point Sets 3

Reconstructing a 3D shape from sample points is a central problem faced in medical applications, reverse engineering, natural sciences, cultural heritage projects, etc. While these applications motivated intense research on 3D surface reconstruction, the problem of reconstructing more general shapes hardly received any attention. This paper develops a reconstruction algorithm changing the 3D reconstruction paradigm as follows. First, the algorithm handles general shapes, i.e. compact sets, as opposed to surfaces. Under mild assumptions on the sampling of the compact set, the reconstruction is proved to be correct in terms of homotopy type. Second, the algorithm does not output a single reconstruction but a nested sequence of plausible reconstructions. Third, the algorithm accommodates topological persistence so as to select the most stable features only. Finally, in case of reconstruction failure, it allows the identification of under-sampled areas, so as to possibly fix the sampling. These key features are illustrated by experimental results on challenging datasets, and should prove instrumental in enhancing the processing of such datasets in the aforementioned applications.