Discrete Differential Geometry (DDG) concerns discrete counterparts of notions and methods in differential geometry. This thesis deals with a core subject in DDG, discrete curvature theories on various types of polyhedral surfaces that are practically important for free-form architecture, sunlight-redirecting shading systems, and face recognition. Modeled as polyhedral surfaces, the shapes of free-form structures may have to satisfy different geometric or physical constraints. We study a combination of geometry and physics – the discrete surfaces that can stand on their own, as well as having proper shapes for the manufacture. These proper shapes, known as circular and conical meshes, are closely related to discrete principal curvatures. We study curvature theories that make such surfaces possible. Shading systems of freeform building skins are new types of energy-saving structures that can re-direct the sunlight. From these systems, discrete line congruences across polyhedral surfaces can be abstracted. We develop a new curvature theory for polyhedral surfaces equipped with normal congruences – a particular type of congruences defined by linear interpolation of vertex normals. The main results are a discussion of various definitions of normality, a detailed study of the geometry of such congruences, and a concept of curvatures and shape operators associated with the faces of a triangle mesh. These curvatures are compatible with both normal congruences and the Steiner formula. In addition to architecture, we consider the role of discrete curvatures in face recognition. We use geometric measure theory to introduce the notion of asymptotic cones associated with a singular subspace of a Riemannian manifold, which is an extension of the classical notion of asymptotic directions. We get a simple expression of these cones for polyhedral surfaces, as well as convergence and approximation theorems. We use the asymptotic cones as facial descriptors and demonstrate the practicability and accuracy of their applications in face recognition.

Dans cette thèse, nous développons un modèle du second ordre pour la représentation des formes (courbes et surfaces) grâce à la théorie des cycles normaux. Le cycle normal d'une forme est le courant associé à son fibré normal. En introduisant des métriques à noyaux sur les cycles normaux, nous obtenons une mesure de dissimilarité entre formes qui prend en compte leurs courbures. Cette mesure est ensuite utilisée comme terme d'attache aux données dans une optique d'appariement et d'analyse de formes par les déformations. Le chapitre 1 est une revue du domaine de l'analyse de formes par les déformations. Nous insistons plus particulièrement sur la mise en place théorique et numérique du modèle de Large Deformation Diffeomorphic Metric Mapping (LDDMM). Le chapitre 2 se concentre sur la représentation des formes par les cycles normaux dans un cadre unifié qui englobe à la fois les formes continues et discrètes. Nous précisons dans quelle mesure cette représentation contient des informations de courbure. Enfin nous montrons le lien entre le cycle normal d'une forme et son varifold. Dans le chapitre 3, nous introduisons les métriques à noyaux. Ainsi, nous pouvons considérer les cycles normaux dans un espace de Hilbert avec un produit scalaire explicite. Nous détaillons ce produit scalaire dans le cas des courbes et surfaces discrètes avec certains noyaux, ainsi que le gradient associé. Nous montrons enfin que malgré le choix de noyaux simples, nous ne perdons pas toutes les informations de courbures. Le chapitre 4 utilise cette nouvelle métrique comme terme d'attache aux données dans le cadre LDDMM. Nous présentons de nombreux appariements et estimations de formes moyennes avec des courbes ou des surfaces. L'objectif de ce chapitre est d'illustrer les différentes propriétés des cycles normaux pour l'analyse des déformations sur des exemples synthétiques et réels. / In this thesis, we develop a second order model for the representation of shapes (curves or surfaces) using the theory of normal cycles. The normal cycle of a shape is the current associated with its normal bundle. Introducing kernel metrics on normal cycles, we obtain a dissimilarity measure between shapes which takes into account curvature. This measure is used as a data attachment term for a purpose of registration and shape analysis by deformations. Chapter 1 is a review of the field of shape analysis. We focus on the setting of the theoretical and numerical model of the Large Deformation Diffeomorphic Metric Mapping(LDDMM).Chapter 2 focuses on the representation of shapes with normal cycles in a unified framework that encompasses both the continuous and the discrete shapes. We specify to what extend this representation encodes curvature information. Finally, we show the link between the normal cycle of a shape and its varifold. In chapter 3, we introduce the kernel metrics, so that we can consider normal cycles in a Hilbert space with an explicit scalar product. We detail this scalar product for discrete curves and surfaces with some kernels, as well as the associated gradient. We show that even with simple kernels, we do not get rid of all the curvature informations. The chapter 4 introduces this new metric as a data attachment term in the framework of LDDMM. We present numerous registrations and mean shape estimation for curves and surfaces. The aim of this chapter is to illustrate the different properties of normal cycles for the deformations analysis on synthetic and real examples.