Relief extraction and editing

Bas-reliefs are widely used in the world around us, for example, on coinage, for branding products, and for sculptural decoration. Reverse engineering of reliefs–extracting existing reliefs from input surfaces–makes it possible to apply them to new items; relief editing tools allow modification of reverse-engineered reliefs. This paper presents a novel approach to relief extraction based on differential coordinates, which offers advantages of speed and precise extraction. It also gives the first method in the literature specifically designed for relief editing. The base surface is estimated using normal smoothing and Poisson reconstruction, allowing a relief (which may lie on a smooth or textured input surface) to be automatically extracted by height thresholding. We also provide a range of relief editing tools, also using differential coordinates, permitting both global transformations (translation, rotation, and scaling) of the whole relief, as well as local modifications to the relief. Our relief editing algorithm, unlike generic mesh editing algorithms, is specifically designed to preserve the geometric detail of the relief over the base surface. The effectiveness of our methods is demonstrated on various examples of real industrial interest

Similarity reasoning for local surface analysis and recognition

This thesis addresses the similarity assessment of digital shapes, contributing to the analysis of surface characteristics that are independent of the global shape but are crucial to identify a model as belonging to the same manufacture, the same origin/culture or the same typology (color, common decorations, common feature elements, compatible style elements, etc.). To face this problem, the interpretation of the local surface properties is crucial. We go beyond the retrieval of models or surface patches in a collection of models, facing the recognition of geometric patterns across digital models with different overall shape. To address this challenging problem, the use of both engineered and learning-based descriptions are investigated, building one of the first contributions towards the localization and identification of geometric patterns on digital surfaces. Finally, the recognition of patterns adds a further perspective in the exploration of (large) 3D data collections, especially in the cultural heritage domain. Our work contributes to the definition of methods able to locally characterize the geometric and colorimetric surface decorations. Moreover, we showcase our benchmarking activity carried out in recent years on the identification of geometric features and the retrieval of digital models completely characterized by geometric or colorimetric patterns

3D updating of solid models based on local geometrical meshes applied to the reconstruction of ancient monumental structures

We introduce a novel methodology for locally updating an existing 3D solid model of a complex monumental structure with the geometric information provided by a 3D mesh (point cloud) extracted from the digital survey of a specific sector of a monument. Solid models are fundamental for engineering analysis and conservation of monumental structures of the cultural heritage. Finite elements analysis (FEA), the most versatile and commonly used tool for the numerical simulation of the static and dynamic response of large structures, requires 3D solids which accurately represent the outside as well as the inside geometry and topology of the domain to be analyzed. However, the structural changes introduced during the lifetime of the monument and the damage caused by anthropogenic and natural factors contribute to producing complex geometrical configurations that may not be generated with the desired accuracy in standard CAD solid modeling software. On the other hand, the development of digital techniques for surveying historical buildings and cultural monuments, such as laser scanning and photogrammetric reconstruction, has made possible the creation of accurate 3D mesh models describing the geometry of those structures for multiple applications in heritage documentation, preservation, and archaeological interpretations. The proposed methodology consists of a series of procedures which utilize image processing, computer vision, and computational geometry algorithms operating on entities defined in the Solid Modeling space and the Mesh space. The operand solid model is defined as the existing solid model to be updated. The 3D mesh model containing new surface information is first aligned to the operand solid model via 3D registration and, subsequently, segmented and converted to a provisional solid model incorporating the features to be added or subtracted. Finally, provisional and operand models are combined and data is transferred through regularized Boolean operations performed in a standard CAD environment. We test the procedure on the Main Platform of the Huaca de la Luna, Trujillo, Peru, one of the most important massive earthen structures of the Moche civilization. Solid models are defined in AutoCAD while 3D meshes are recorded with a Faro Focus laser scanner. The results indicate that the proposed methodology is effective at transferring complex geometrical and topological features from the mesh to the solid modeling space. The methodology preserves, as much as possible, the initial accuracy of meshes on the geometry of the resultant solid model which would be highly difficult and time consuming using manual approaches.Tesi

Discovering structural regularity in 3D geometry

We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or meshbased models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis. © 2008 ACM

Of assembling small sculptures and disassembling large geometry

This thesis describes the research results and contributions that have been achieved during the author’s doctoral work. It is divided into two independent parts, each of which is devoted to a particular research aspect. The first part covers the true-to-detail creation of digital pieces of art, so-called relief sculptures, from given 3D models. The main goal is to limit the depth of the contained objects with respect to a certain perspective without compromising the initial three-dimensional impression. Here, the preservation of significant features and especially their sharpness is crucial. Therefore, it is necessary to overemphasize fine surface details to ensure their perceptibility in the more complanate relief. Our developments are aimed at amending the flexibility and user-friendliness during the generation process. The main focus is on providing real-time solutions with intuitive usability that make it possible to create precise, lifelike and aesthetic results. These goals are reached by a GPU implementation, the use of efficient filtering techniques, and the replacement of user defined parameters by adaptive values. Our methods are capable of processing dynamic scenes and allow the generation of seamless artistic reliefs which can be composed of multiple elements. The second part addresses the analysis of repetitive structures, so-called symmetries, within very large data sets. The automatic recognition of components and their patterns is a complex correspondence problem which has numerous applications ranging from information visualization over compression to automatic scene understanding. Recent algorithms reach their limits with a growing amount of data, since their runtimes rise quadratically. Our aim is to make even massive data sets manageable. Therefore, it is necessary to abstract features and to develop a suitable, low-dimensional descriptor which ensures an efficient, robust, and purposive search. A simple inspection of the proximity within the descriptor space helps to significantly reduce the number of necessary pairwise comparisons. Our method scales quasi-linearly and allows a rapid analysis of data sets which could not be handled by prior approaches because of their size.Die vorgelegte Arbeit beschreibt die wissenschaftlichen Ergebnisse und Beiträge, die während der vergangenen Promotionsphase entstanden sind. Sie gliedert sich in zwei voneinander unabhängige Teile, von denen jeder einem eigenen Forschungsschwerpunkt gewidmet ist. Der erste Teil beschäftigt sich mit der detailgetreuen Erzeugung digitaler Kunstwerke, sogenannter Reliefplastiken, aus gegebenen 3D-Modellen. Das Ziel ist es, die Objekte, abhängig von der Perspektive, stark in ihrer Tiefe zu limitieren, ohne dass der Eindruck der räumlichen Ausdehnung verloren geht. Hierbei kommt dem Aufrechterhalten der Schärfe signifikanter Merkmale besondere Bedeutung zu. Dafür ist es notwendig, die feinen Details der Objektoberfläche überzubetonen, um ihre Sichtbarkeit im flacheren Relief zu gewährleisten. Unsere Weiterentwicklungen zielen auf die Verbesserung der Flexibilität und Benutzerfreundlichkeit während des Enstehungsprozesses ab. Der Fokus liegt dabei auf dem Bereitstellen intuitiv bedienbarer Echtzeitlösungen, die die Erzeugung präziser, naturgetreuer und visuell ansprechender Resultate ermöglichen. Diese Ziele werden durch eine GPU-Implementierung, den Einsatz effizienter Filtertechniken sowie das Ersetzen benutzergesteuerter Parameter durch adaptive Werte erreicht. Unsere Methoden erlauben das Verarbeiten dynamischer Szenen und die Erstellung nahtloser, kunstvoller Reliefs, die aus mehreren Elementen und Perspektiven zusammengesetzt sein können. Der zweite Teil behandelt die Analyse wiederkehrender Stukturen, sogenannter Symmetrien, innerhalb sehr großer Datensätze. Das automatische Erkennen von Komponenten und deren Muster ist ein komplexes Korrespondenzproblem mit zahlreichen Anwendungen, von der Informationsvisualisierung über Kompression bis hin zum automatischen Verstehen. Mit zunehmender Datenmenge geraten die etablierten Algorithmen an ihre Grenzen, da ihre Laufzeiten quadratisch ansteigen. Unser Ziel ist es, auch massive Datensätze handhabbar zu machen. Dazu ist es notwendig, Merkmale zu abstrahieren und einen passenden niedrigdimensionalen Deskriptor zu entwickeln, der eine effiziente, robuste und zielführende Suche erlaubt. Eine simple Betrachtung der Nachbarschaft innerhalb der Deskriptoren hilft dabei, die Anzahl notwendiger paarweiser Vergleiche signifikant zu reduzieren. Unser Verfahren skaliert quasi-linear und ermöglicht somit eine rasche Auswertung auch auf Daten, die für bisherige Methoden zu groß waren

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

Automatic Mesh-Based Segmentation of Multiple Organs in MR Images

La segmentation de structures anatomiques multiples dans des images de résonance magnétique (RM) est souvent requise dans des applications de génie biomédical telles que la simulation numérique, la chirurgie guidée par l’image, la planification de traitements, etc. De plus, il y a un besoin croissant pour une segmentation automatique d’organes multiples et de structures complexes à partir de cette modalité d’imagerie. Il existe plusieurs techniques de segmentation multi-objets qui ont été appliquées avec succès sur des images de tomographie axiale à rayons-X (CT). Cependant, dans le cas des images RM cette tâche est plus difficile en raison de l’inhomogénéité des intensités dans ces images et de la variabilité dans l’apparence des structures anatomiques. Par conséquent, l’état de l’art sur la segmentation multi-objets sur des images RM est beaucoup plus faible que celui sur les images CT. Parmi les travaux qui portent sur la segmentation d’images RM, les approches basées sur la segmentation de régions sont sensibles au bruit et la non uniformité de l’intensité dans les images. Les approches basées sur les contours ont de la difficulté à regrouper les informations sur les contours de sorte à produire un contour fermé cohérent. Les techniques basées sur les atlas peuvent avoir des problèmes en présence de structures complexes avec une grande variabilité anatomique. Les modèles déformables représentent une des méthodes les plus populaire pour la détection automatique de différents organes dans les images RM. Cependant, ces modèles souffrent encore d’une limitation importante qui est leur sensibilité à la position initiale et la forme du modèle. Une initialisation inappropriée peut conduire à un échec dans l’extraction des frontières des objets. D’un autre côté, le but ultime d’une segmentation automatique multi-objets dans les images RM est de produire un modèle qui peut aider à extraire les caractéristiques structurelles d’organes distincts dans les images. Les méthodes d’initialisation automatique actuelles qui utilisent différents descripteurs ne réussissent pas complètement l’extraction d’objets multiples dans les images RM. Nous avons besoin d’exploiter une information plus riche qui se trouve dans les contours des organes. Dans ce contexte les maillages adaptatifs anisotropiques semblent être une solution potentielle au problème soulevé. Les maillages adaptatifs anisotropiques construits à partir des images RM contiennent de l’information à un plus haut niveau d’abstraction représentant les éléments, d’une orientation et d’une forme donnée, qui constituent les différents organes dans l’image. Les méthodes existantes pour la construction de maillages adaptatifs sont basées sur les intensités dans l’image et possèdent une limitation pratique qui est l’alignement inadéquat des éléments du maillage en présence de contours inclinés dans l’image. Par conséquent, nous avons aussi besoin d’améliorer le processus d’adaptation de maillage pour produire une meilleure représentation de l’image basée sur un maillage.----------ABSTRACT: Segmentation of multiple anatomical structures in MR images is often required for biomedical engineering applications such as clinical simulation, image-guided surgery, treatment planning, etc. Moreover, there is a growing need for automatic segmentation of multiple organs and complex structures from this medical imaging modality. Many successful multi-object segmentation attempts were introduced for CT images. However in the case of MR images it is a more challenging task due to intensity inhomogeneity and variability of anatomy appearance. Therefore, state-of-the-art in multi-object MR segmentation is very inferior to that of CT images. In literature dealing with MR image segmentation, the region-based approaches are sensitive to noise and non-uniformity in the input image. The edge-based approaches are challenging to group the edge information into a coherent closed contour. The atlas-based techniques can be problematic for complicated structures with anatomical variability. Deformable models are among the most popular methods for automatic detection of different organs in MR images. However they still have an important limitation which is that they are sensitive to initial position and shape of the model. An unsuitable initialization may provide failure to capture the true boundaries of the objects. On the other hand, a useful aim for an automatic multi-object MR segmentation is to provide a model which promotes understanding of the structural features of the distinct objects within the MR images. The current automatic initialization methods which have used different descriptors are not completely successful in extracting multiple objects from MR images and we need to find richer information that is available from edges. In this regard, anisotropic adaptive meshes seem to be a potential solution to the aforesaid limitation. Anisotropic adaptive meshes constructed from MR images contain higher level, abstract information about the anatomical structures of the organs within the image retained as the elements shape and orientation. Existing methods for constructing adaptive meshes based on image features have a practical limitation where manifest itself in inadequate mesh elements alignment to inclined edges in the image. Therefore, we also have to enhance mesh adaptation process to provide a better mesh-based representation. In this Ph.D. project, considering the highlighted limitations we are going to present a novel method for automatic segmentation of multiple organs in MR images by incorporating mesh adaptation techniques. In our progress, first, we improve an anisotropic adaptation process for the meshes that are constructed from MR images where the mesh elements align adequately to the image content and improve mesh anisotropy along edges in all directions. Then the resulting adaptive meshes are used for initialization of multiple active models which leads to extract initial object boundaries close to the true boundaries of multiple objects simultaneously. Finally, the Vector Field Convolution method is utilized to guide curve evolution towards the object boundaries to obtain the final segmentation results and present a better performance in terms of speed and accuracy

Scales and Scale-like Structures

Scales are a visually striking feature that grows on many animals. These small, rigid plates embedded in the skin form an integral part of our description of fish and reptiles, some plants, and many extinct animals. Scales exist in many shapes and sizes, and serve as protection, camouflage, and plumage for animals. The variety of scales and the animals they grow from pose an interesting problem in the field of Computer Graphics. This dissertation presents a method for generating scales and scale-like structures on a polygonal mesh through surface replacement. A triangular mesh was covered with scales and one or more proxy-models were used as the scales shape. A user began scale generation by drawing a lateral line on the model to control the distribution and orientation of scales on the surface. Next, a vector field was created over the surface to control an anisotropic Voronoi tessellation, which represents the region occupied by each scale. Then these regions were replaced by cutting the proxy model to match the boundary of the Voronoi region and deform the cut model onto the surface. The final result is a fully connected 2-manifold that is suitable for subsequent post-processing applications, like surface subdivision

Uses of uncalibrated images to enrich 3D models information

The decrease in costs of semi-professional digital cameras has led to the possibility for everyone to acquire a very detailed description of a scene in a very short time. Unfortunately, the interpretation of the images is usually quite hard, due to the amount of data and the lack of robust and generic image analysis methods. Nevertheless, if a geometric description of the depicted scene is available, it gets much easier to extract information from 2D data. This information can be used to enrich the quality of the 3D data in several ways. In this thesis, several uses of sets of unregistered images for the enrichment of 3D models are shown. In particular, two possible fields of application are presented: the color acquisition, projection and visualization and the geometry modification. Regarding color management, several practical and cheap solutions to overcome the main issues in this field are presented. Moreover, some real applications, mainly related to Cultural Heritage, show that provided methods are robust and effective. In the context of geometry modification, two approaches are presented to modify already existing 3D models. In the first one, information extracted from images is used to deform a dummy model to obtain accurate 3D head models, used for simulation in the context of three-dimensional audio rendering. The second approach presents a method to fill holes in 3D models, with the use of registered images depicting a pattern projected on the real object. Finally, some useful indications about the possible future work in all the presented fields are given, in order to delineate the developments of this promising direction of research