Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison

Visualizing shape transformation between chimpanzee and human braincases

The quantitative comparison of the form of the braincase is a central issue in paleoanthropology (i.e., the study of human evolution based on fossil evidence). The major difficulty is that there are only few locations defining biological correspondence between individual braincases. In this paper, we use mesh parameterization techniques to tackle this problem. We propose a method to conformally parameterize the genus-0 surface of the braincase on the sphere and to calibrate the parameterization to match biological constraints. The resulting consistent parameterization gives detailed information about shape differences between the braincase of human and chimp. This opens up new perspectives for the quantitative comparison of "featureless” biological structure

Geometric modeling and optimization over regular domains for graphics and visual computing

The effective construction of parametric representation of complicated geometric objects can facilitate many design, analysis, and simulation tasks in Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). Given a 3D shape, the procedure of finding such a parametric representation upon a canonical domain is called geometric parameterization. Regular geometric regions, such as polycubes and spheres, are desirable domains for parameterization. Parametric representations defined upon regular geometric domains have many desirable mathematical properties and can facilitate or simplify various surface/solid modeling and processing computation. This dissertation studies the construction of parameterization on regular geometric domains and explores their applications in shape modeling and computer-aided design. Specifically, we studies (1) the surface parameterization on the spherical domain for closed genus-zero surfaces; (2) the surface parameterization on the polycube domain for general closed surfaces; and (3) the volumetric parameterization for 3D-manifolds embedded in 3D Euclidean space. We propose novel computational models to solve these geometric problems. Our computational models reduce to nonlinear optimizations with various geometric constraints. Hence, we also need to explore effective optimization algorithms. The main contributions of this dissertation are three-folded. (1) We developed an effective progressive spherical parameterization algorithm, with an efficient nonlinear optimization scheme subject to the spherical constraint. Compared with the state-of-the-art spherical mapping algorithms, our algorithm demonstrates the advantages of great efficiency, lower distortion, and guaranteed bijectiveness, and we show its applications in spherical harmonic decomposition and shape analysis. (2) We propose a first topology-preserving polycube domain optimization algorithm that simultaneously optimizes polycube domain together with the parameterization to balance the mapping distortion and domain simplicity. We develop effective nonlinear geometric optimization algorithms dealing with variables with and without derivatives. This polycube parameterization algorithm can benefit the regular quadrilateral mesh generation and cross-surface parameterization. (3) We develop a novel quaternion-based optimization framework for 3D frame field construction and volumetric parameterization computation. We demonstrate our constructed 3D frame field has better smoothness, compared with state-of-the-art algorithms, and is effective in guiding low-distortion volumetric parameterization and high-quality hexahedral mesh generation

The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation

Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of surgeries. Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly. This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation. The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images

Métamorphose de maillage 3D

Cette thèse de doctorat aborde spécifiquement le problème de la métamorphose entre différents maillages 3D, qui peut assurer un niveau élevé de qualité pour la séquence de transition, qui devrait être aussi lisse et progressive que possible, cohérente par rapport à la géométrie et la topologie, et visuellement agréable. Les différentes étapes impliquées dans le processus de transformation sont développées dans cette thèse. Nos premières contributions concernent deux approches différentes des paramétrisations: un algorithme de mappage barycentrique basé sur la préservation des rapports de longueur et une technique de paramétrisation sphérique, exploitant la courbure Gaussien. L'évaluation expérimentale, effectuées sur des modèles 3D de formes variées, démontré une amélioration considérable en termes de distorsion maillage pour les deux méthodes. Afin d aligner les caractéristiques des deux modèles d'entrée, nous avons considéré une technique de déformation basée sur la fonction radial CTPS C2a approprié pour déformer le mappage dans le domaine paramétrique et maintenir un mappage valide a travers le processus de mouvement. La dernière contribution consiste d une une nouvelle méthode qui construit un pseudo metamaillage qui évite l'exécution et le suivi des intersections d arêtes comme rencontrées dans l'état-of-the-art. En outre, notre méthode permet de réduire de manière drastique le nombre de sommets normalement nécessaires dans une structure supermesh. Le cadre générale de métamorphose a été intégré dans une application prototype de morphing qui permet à l'utilisateur d'opérer de façon interactive avec des modèles 3D et de contrôler chaque étape du processusThis Ph.D. thesis specifically deals with the issue of metamorphosis of 3D objects represented as 3D triangular meshes. The objective is to elaborate a complete 3D mesh morphing methodology which ensures high quality transition sequences, smooth and gradual, consistent with respect to both geometry and topology, and visually pleasant. Our first contributions concern the two different approaches of parameterization: a new barycentric mapping algorithm based on the preservation of the mesh length ratios, and a spherical parameterization technique, exploiting a Gaussian curvature criterion. The experimental evaluation, carried out on 3D models of various shapes, demonstrated a considerably improvement in terms of mesh distortion for both methods. In order to align the features of the two input models, we have considered a warping technique based on the CTPS C2a radial basis function suitable to deform the models embeddings in the parametric domain maintaining a valid mapping through the entire movement process. We show how this technique has to be adapted in order to warp meshes specified in the parametric domains. A final contribution consists of a novel algorithm for constructing a pseudo-metamesh that avoids the complex process of edge intersections encountered in the state-of-the-art. The obtained mesh structure is characterized by a small number of vertices and it is able to approximate both the source and target shapes. The entire mesh morphing framework has been integrated in an interactive application that allows the user to control and visualize all the stages of the morphing processEVRY-INT (912282302) / SudocSudocFranceF

Abstract. Consistent Spherical Parameterization

Many applications benefit from surface parameterization, including texture mapping, morphing, remeshing, compression, object recognition, and detail transfer, because processing is easier on the domain than on the original irregular mesh. We present a method for simultaneously parameterizing several genus-0 meshes possibly with boundaries onto a common spherical domain, while ensuring that corresponding user-highlighted features on each of the meshes map to the same domain locations. We obtain visually smooth parameterizations without any cuts, and the constraints enable us to directly associate semantically important features such as animal limbs or facial detail. Our method is robust and works well with either sparse or dense sets of constraints. Fig 1. Consistent Spherical Parameterization of a collection of heads