A Segmentation Transfer Method for Articulated Models

The definitive version is available at digilib.eg.orgInternational audienceIn this paper, we propose using a pre-segmented example model to perform semantic-oriented segmentation of non-rigid 3D models of the same class (human, octopus, quadrupeds, etc.). Using the fact that the same type of non-rigid models share the same global topological structure, we exploit coarse topological shape attributes in conjunction with a seed-based segmentation approach to transfer a meaningful and consistent segmentation from the example to the target models. Promising results of inter-shape segmentation transfer are shown and discussed for different classes of models

Learning Boundary Edges for 3D-Mesh Segmentation

International audienceThis paper presents a 3D-mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D-meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state-of-the-art

Human perception-oriented segmentation for triangle meshes

A segmentação de malhas é um tópico importante de investigação em computação gráfica, em particular em modelação geométrica. Isto deve-se ao facto de as técnicas de segmentaçãodemalhasteremváriasaplicações,nomeadamentenaproduçãodefilmes, animaçãoporcomputador, realidadevirtual, compressãodemalhas, assimcomoemjogosdigitais. Emconcreto, asmalhastriangularessãoamplamenteusadasemaplicações interativas, visto que sua segmentação em partes significativas (também designada por segmentação significativa, segmentação perceptiva ou segmentação perceptualmente significativa ) é muitas vezes vista como uma forma de acelerar a interação com o utilizador ou a deteção de colisões entre esses objetos 3D definidos por uma malha, bem como animar uma ou mais partes significativas (por exemplo, a cabeça de uma personagem) de um dado objeto, independentemente das restantes partes. Acontece que não se conhece nenhuma técnica capaz de segmentar correctamente malhas arbitrárias −ainda que restritas aos domínios de formas livres e não-livres− em partes significativas. Algumas técnicas são mais adequadas para objetos de forma não-livre (por exemplo, peças mecânicas definidas geometricamente por quádricas), enquanto outras são mais talhadas para o domínio dos objectos de forma livre. Só na literatura recente surgem umas poucas técnicas que se aplicam a todo o universo de objetos de forma livre e não-livre. Pior ainda é o facto de que a maioria das técnicas de segmentação não serem totalmente automáticas, no sentido de que quase todas elas exigem algum tipo de pré-requisitos e assistência do utilizador. Resumindo, estes três desafios relacionados com a proximidade perceptual, generalidade e automação estão no cerne do trabalho descrito nesta tese. Para enfrentar estes desafios, esta tese introduz o primeiro algoritmo de segmentação baseada nos contornos ou fronteiras dos segmentos, cuja técnica se inspira nas técnicas de segmentação baseada em arestas, tão comuns em análise e processamento de imagem,porcontraposiçãoàstécnicasesegmentaçãobaseadaemregiões. Aideiaprincipal é a de encontrar em primeiro lugar a fronteira de cada região para, em seguida, identificar e agrupar todos os seus triângulos internos. As regiões da malha encontradas correspondem a saliências e reentrâncias, que não precisam de ser estritamente convexas, nem estritamente côncavas, respectivamente. Estas regiões, designadas regiões relaxadamenteconvexas(ousaliências)eregiõesrelaxadamentecôncavas(oureentrâncias), produzem segmentações que são menos sensíveis ao ruído e, ao mesmo tempo, são mais intuitivas do ponto de vista da perceção humana; por isso, é designada por segmentação orientada à perceção humana (ou, human perception- oriented (HPO), do inglês). Além disso, e ao contrário do atual estado-da-arte da segmentação de malhas, a existência destas regiões relaxadas torna o algoritmo capaz de segmentar de maneira bastante plausível tanto objectos de forma não-livre como objectos de forma livre. Nesta tese, enfrentou-se também um quarto desafio, que está relacionado com a fusão de segmentação e multi-resolução de malhas. Em boa verdade, já existe na literatura uma variedade grande de técnicas de segmentação, bem como um número significativo de técnicas de multi-resolução, para malhas triangulares. No entanto, não é assim tão comum encontrar estruturas de dados e algoritmos que façam a fusão ou a simbiose destes dois conceitos, multi-resolução e segmentação, num único esquema multi-resolução que sirva os propósitos das aplicações que lidam com malhas simples e segmentadas, sendo que neste contexto se entende que uma malha simples é uma malha com um único segmento. Sendo assim, nesta tese descreve-se um novo esquema (entenda-seestruturasdedadosealgoritmos)demulti-resoluçãoesegmentação,designado por extended Ghost Cell (xGC). Este esquema preserva a forma das malhas, tanto em termos globais como locais, ou seja, os segmentos da malha e as suas fronteiras, bem como os seus vincos e ápices são preservados, não importa o nível de resolução que usamos durante a/o simplificação/refinamento da malha. Além disso, ao contrário de outros esquemas de segmentação, tornou-se possível ter segmentos adjacentes com dois ou mais níveis de resolução de diferença. Isto é particularmente útil em animação por computador, compressão e transmissão de malhas, operações de modelação geométrica, visualização científica e computação gráfica. Em suma, esta tese apresenta um esquema genérico, automático, e orientado à percepção humana, que torna possível a simbiose dos conceitos de segmentação e multiresolução de malhas trianguladas que sejam representativas de objectos 3D.The mesh segmentation is an important topic in computer graphics, in particular in geometric computing. This is so because mesh segmentation techniques find many applications in movies, computer animation, virtual reality, mesh compression, and games. Infact, trianglemeshesarewidelyusedininteractiveapplications, sothattheir segmentation in meaningful parts (i.e., human-perceptually segmentation, perceptive segmentationormeaningfulsegmentation)isoftenseenasawayofspeedinguptheuser interaction, detecting collisions between these mesh-covered objects in a 3D scene, as well as animating one or more meaningful parts (e.g., the head of a humanoid) independently of the other parts of a given object. It happens that there is no known technique capable of correctly segmenting any mesh into meaningful parts. Some techniques are more adequate for non-freeform objects (e.g., quadricmechanicalparts), whileothersperformbetterinthedomainoffreeform objects. Only recently, some techniques have been developed for the entire universe of objects and shapes. Even worse it is the fact that most segmentation techniques are not entirely automated in the sense that almost all techniques require some sort of pre-requisites and user assistance. Summing up, these three challenges related to perceptual proximity, generality and automation are at the core of the work described in this thesis. In order to face these challenges, we have developed the first contour-based mesh segmentation algorithm that we may find in the literature, which is inspired in the edgebased segmentation techniques used in image analysis, as opposite to region-based segmentation techniques. Its leading idea is to firstly find the contour of each region, and then to identify and collect all of its inner triangles. The encountered mesh regions correspond to ups and downs, which do not need to be strictly convex nor strictly concave, respectively. These regions, called relaxedly convex regions (or saliences) and relaxedly concave regions (or recesses), produce segmentations that are less-sensitive to noise and, at the same time, are more intuitive from the human point of view; hence it is called human perception- oriented (HPO) segmentation. Besides, and unlike the current state-of-the-art in mesh segmentation, the existence of these relaxed regions makes the algorithm suited to both non-freeform and freeform objects. In this thesis, we have also tackled a fourth challenge, which is related with the fusion of mesh segmentation and multi-resolution. Truly speaking, a plethora of segmentation techniques, as well as a number of multiresolution techniques, for triangle meshes already exist in the literature. However, it is not so common to find algorithms and data structures that fuse these two concepts, multiresolution and segmentation, into a symbiotic multi-resolution scheme for both plain and segmented meshes, in which a plainmeshisunderstoodasameshwithasinglesegment. So, weintroducesuchanovel multiresolution segmentation scheme, called extended Ghost Cell (xGC) scheme. This scheme preserves the shape of the meshes in both global and local terms, i.e., mesh segments and their boundaries, as well as creases and apices are preserved, no matter the level of resolution we use for simplification/refinement of the mesh. Moreover, unlike other segmentation schemes, it was made possible to have adjacent segments with two or more resolution levels of difference. This is particularly useful in computer animation, mesh compression and transmission, geometric computing, scientific visualization, and computer graphics. In short, this thesis presents a fully automatic, general, and human perception-oriented scheme that symbiotically integrates the concepts of mesh segmentation and multiresolution

Analyse de maillages surfaciques par construction et comparaison de modèles moyens et par décomposition par graphes s’appuyant sur les courbures discrètes : application à l’étude de la cornée humaine

Réalisé en cotutelle avec Aix Marseille Université.Cette thèse se découpe en trois parties. Les deux premières portent sur le développement de méthodes pour la construction de modèles géométriques moyens et pour la comparaison de modèles. Ces approches sont appliquées à la cornée humaine pour l’élaboration d’atlas et pour l’étude biométrique robuste. La troisième partie porte sur une méthode générique d'extraction d'informations dans un maillage en s'appuyant sur des propriétés différentielles discrètes afin de construire une structure par graphe permettant l'extraction de caractéristiques par une description sémantique. Les atlas anatomiques conventionnels (papier ou CD-ROM) sont limités par le fait qu'ils montrent généralement l'anatomie d'un seul individu qui ne représente pas nécessairement bien la population dont il est issu. Afin de remédier aux limitations des atlas conventionnels, nous proposons dans la première partie d’élaborer un atlas numérique 3D contenant les caractéristiques moyennes et les variabilités de la morphologie d'un organe, plus particulièrement de la cornée humaine. Plusieurs problématiques sont abordées, telles que la construction d'une cornée moyenne et la comparaison de cornées. Il existe à ce jour peu d'études ayant ces objectifs car la mise en correspondance de surfaces cornéennes est une problématique non triviale. En plus d'aider à développer une meilleure connaissance de l'anatomie cornéenne, la modélisation 3D de la cornée normale permet de détecter tout écart significatif par rapport à la "normale" permettant un diagnostic précoce de pathologies ou anomalies de la forme de la cornée. La seconde partie a pour objectif de développer une méthode pour reconnaître une surface parmi un groupe de surfaces à l’aide de leurs acquisitions 3D respectives, dans le cadre d’une application de biométrie sur la cornée. L’idée est de quantifier la différence entre chaque surface et une surface donnée, et de déterminer un seuil permettant la reconnaissance. Ce seuil est dépendant des variations normales au sein d’un même sujet, et du bruit inhérent à l’acquisition. Les surfaces sont rognées et trouées de façon imprévisible, de plus il n’y a pas de point de mise en correspondance commun aux surfaces. Deux méthodes complémentaires sont proposées. La première consiste à calculer le volume entre les surfaces après avoir effectué un recalage, et à utiliser ce volume comme un critère de similarité. La seconde approche s’appuie sur une décomposition en harmoniques sphériques en utilisant les coefficients comme des descripteurs de forme, qui permettront de comparer deux surfaces. Des résultats sont présentés pour chaque méthode en les comparant à la méthode la plus récemment décrite dans la littérature, les avantages et inconvénients de chacune sont détaillés. Une méthodologie en cascade utilisant ces deux méthodes afin de combiner les avantages de chacune est aussi proposée. La troisième et dernière partie porte sur une nouvelle méthode de décomposition en graphes de maillages 3D triangulés. Nous utilisons des cartes de courbures discrètes comme descripteur de forme afin de découper le maillage traité en huit différentes catégorie de carreaux (ou peak, ridge, saddle ridge, minimal, saddle valley, valley, pit et flat). Ensuite, un graphe d'adjacence est construit avec un nœud pour chaque carreau. Toutes les catégories de carreaux ne pouvant pas être adjacentes dans un contexte continu, des jonctions intermédiaires sont ajoutées afin d'assurer une cohérence continue entre les zones. Ces graphes sont utilisés pour extraire des caractéristiques géométriques décrites par des motifs (ou patterns), ce qui permet de détecter des régions spécifiques dans un modèle 3D, ou des motifs récurrents. Cette méthode de décomposition étant générique, elle peut être appliquée à de nombreux domaines où il est question d’analyser des modèles géométriques, en particulier dans le contexte de la cornée.This thesis comprises three parts. The first two parts concern the development of methods for the construction of mean geometric models and for model comparison. These approaches are applied to the human cornea for the construction of atlases and a robust biometric study. The third part focuses on a generic method for the extraction of information in a mesh. This approach is based on discrete differential properties for building a graph structure to extract features using a semantic description. Conventional anatomical atlases (paper or CD-ROM) are limited by the fact they generally show the anatomy of a single individual who does not necessarily represent the population from which they originate. To address the limitations of conventional atlases, we propose in the first part of this thesis to construct a 3D digital atlas containing the average characteristics and variability of the morphology of an organ, especially that of the human cornea. Several issues are addressed, such as the construction of an average cornea and the comparison of corneas. Currently, there are few studies with these objectives because the matching of corneal surfaces is a non-trivial problem. In addition to help to develop a better understanding of the corneal anatomy, 3D models of normal corneas can be used to detect any significant deviation from the norm, thereby allowing for an early diagnosis of diseases or abnormalities using the shape of the cornea. The second part of this thesis aims to develop a method for recognizing a surface from a group of surfaces using their 3D acquisitions in a biometric application pertinent to the cornea. The concept behind this method is to quantify the difference between each surface and a given surface and to determine the threshold for recognition. This threshold depends on normal variations within the same subject and noise due to the acquisition system. The surfaces are randomly trimmed and pierced ; moreover, there is no common landmark on the surfaces. Two complementary methods are proposed. The first method consists of the computation of the volume between the surfaces after performing geometrical matching and the use of this volume as a criterion of similarity. The second approach is based on a decomposition of the surfaces into spherical harmonics using the coefficients as shape descriptors to compare the two surfaces. Each result of the proposed methods is compared to the most recent method described in the literature, with the benefits and disadvantages of each one described in detail. A cascading methodology using both methods to combine the advantages of each method is also proposed. The third and final part of this thesis focuses on a new method for decomposing 3D triangulated meshes into graphs. We use discrete curvature maps as the shape descriptor to split the mesh in eight different categories (peak, ridge, saddle ridge, minimal, saddle valley, valley, pit and flat). Next, an adjacency graph is built with a node for each patch. Because all categories of patches cannot be adjacent in a continuous context, intermediate junctions are added to ensure the continuous consistency between patches. These graphs are used to extract geometric characteristics described by patterns that allow for the detection of specific regions in a 3D model or recurrent characteristics. This decomposition method, being generic, can be used in many applications to analyze geometric models, especially in the context of the cornea

Part-based recognition of 3-D objects with application to shape modeling in hearing aid manufacturing

In order to meet the needs of people with hearing loss today hearing aids are custom designed. Increasingly accurate 3-D scanning technology has contributed to the transition from conventional production scenarios to software based processes. Nonetheless, there is a tremendous amount of manual work involved to transform an input 3-D surface mesh of the outer ear into a final hearing aid shape. This manual work is often cumbersome and requires lots of experience which is why automatic solutions are of high practical relevance. This work is concerned with the recognition of 3-D surface meshes of ear implants. In particular we present a semantic part-labeling framework which significantly outperforms existing approaches for this task. We make at least three contributions which may also be found useful for other classes of 3-D meshes. Firstly, we validate the discriminative performance of several local descriptors and show that the majority of them performs poorly on our data except for 3-D shape contexts. The reason for this is that many local descriptor schemas are not rich enough to capture subtle variations in form of bends which is typical for organic shapes. Secondly, based on the observation that the left and the right outer ear of an individual look very similar we raised the question how similar the ear shapes among arbitrary individuals are? In this work, we define a notion of distance between ear shapes as building block of a non-parametric shape model of the ear to better handle the anatomical variability in ear implant labeling. Thirdly, we introduce a conditional random field model with a variety of label priors to facilitate the semantic part-labeling of 3-D meshes of ear implants. In particular we introduce the concept of a global parametric transition prior to enforce transition boundaries between adjacent object parts with an a priori known parametric form. In this way we were able to overcome the issue of inadequate geometric cues (e.g., ridges, bumps, concavities) as natural indicators for the presence of part boundaries. The last part of this work offers an outlook to possible extensions of our methods, in particular the development of 3-D descriptors that are fast to compute whilst at the same time rich enough to capture the characteristic differences between objects residing in the same class