Customised Rapid Manufactured Parts: Technology and Case Studies from the Custom-Fit Project

The design and manufacture of individually customised products is generally restricted to bespoke clothing or footwear for very wealthy customers. The aim of the Custom-Fit project was to develop a fast, flexible and economically viable route for the manufacture of individually customised parts. These products not only provide improved comfort levels but also provide better functional performance, including enhanced safety for the user. This 4.5 year, European Commission subsidised €16 million project, supported by the EU, involving 30 partners across the breadth of the Europe finished in early 2009. This paper will showcase the technology developed: CAD packages which automate the design process and three new rapid manufacturing methods. It will also include case studies on a range of customised products, including customised Motorcycles helmets. The case studies not only demonstrate the performance benefits of individual customisation but also show the potential for new approaches to product design. More information at www.Custom-Fit.org.Mechanical Engineerin

POINTS CLOUD PRE-PROCESSING AND SAMPLING BASED ON DISTANCE ALGORITHM TECHNIQUE

Although the rapid development of reverse engineering techniques such as a modern 3D laser scanners, but can’t use this techniques immediately to generate a perfect surface model for the scanned parts, due to the huge data, the noisy data which associated to the scanning process, and the accuracy limitation of some scanning devices, so, the present paper present a points cloud pre-processing and sampling algorithms have been proposed based on distance calculations and statistical considerations to simplify the row points cloud which obtained using MATTER and FORM 3D laser scanner as a manner to obtain the required geometrical features and mathematical representation from the row points cloud of the scanned object through detection, isolating, and deleting the noised points. A MATLAB program has been constructed for executing the proposed algorithms implemented using a suggested case study with non-uniform shape. The results were proved the validity of the introduced distance algorithms for pre-processing and sampling process where the proficiency percent for pre-processing was (18.65%) with a single attempt, and the counted deviation value rang with the sampling process was (0.0002-0.3497mm)

Symmetry Detection in Large Scale City Scans

In this report we present a novel method for detecting partial symmetries in very large point clouds of 3D city scans. Unlike previous work, which was limited to data sets of a few hundred megabytes maximum, our method scales to very large scenes. We map the detection problem to a nearestneighbor search in a low-dimensional feature space, followed by a cascade of tests for geometric clustering of potential matches. Our algorithm robustly handles noisy real-world scanner data, obtaining a recognition performance comparable to state-of-the-art methods. In practice, it scales linearly with the scene size and achieves a high absolute throughput, processing half a terabyte of raw scanner data over night on a dual socket commodity PC

Analysis and Manipulation of Repetitive Structures of Varying Shape

Self-similarity and repetitions are ubiquitous in man-made and natural objects. Such structural regularities often relate to form, function, aesthetics, and design considerations. Discovering structural redundancies along with their dominant variations from 3D geometry not only allows us to better understand the underlying objects, but is also beneficial for several geometry processing tasks including compact representation, shape completion, and intuitive shape manipulation. To identify these repetitions, we present a novel detection algorithm based on analyzing a graph of surface features. We combine general feature detection schemes with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect recurring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing is applied to verify that the actual data supports the patterns detected in the feature graphs. We introduce our graph based detection algorithm on the example of rigid repetitive structure detection. Then we extend the approach to allow more general deformations between the detected parts. We introduce subspace symmetries whereby we characterize similarity by requiring the set of repeating structures to form a low dimensional shape space. We discover these structures based on detecting linearly correlated correspondences among graphs of invariant features. The found symmetries along with the modeled variations are useful for a variety of applications including non-local and non-rigid denoising. Employing subspace symmetries for shape editing, we introduce a morphable part model for smart shape manipulation. The input geometry is converted to an assembly of deformable parts with appropriate boundary conditions. Our method uses self-similarities from a single model or corresponding parts of shape collections as training input and allows the user also to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries. We obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using repetition-unaware deformation techniques

Image-Based Modeling of Bridges and Its Applications to Evaluating Resiliency of Transportation Networks

Modern urban areas are heavily dependent on transportation networks to sustain their economic life. Hence, when vital components of a regional network are disrupted, economic losses are inevitable. As evidenced by 1989, Loma Prieta and 1994, Northridge earthquakes, the seismic damages experienced by bridges alone result in extensive traffic delays and rerouting, not only hindering emergency response but also causing indirect economic losses that far surpass the direct cost of damage to infrastructure. Nevertheless, in many areas of the U.S., transportation networks lack the resilience required to sustain the potential demands of natural hazards. Traditional hazard assessment methods, in theory, provide the tools required for predicting the vulnerabilities associated with natural hazards. Nonetheless, due to their abstractions of the complex infrastructure and the coupled regional behavior, they often fall short of that expectation. This study proposes a semi-automated image-based model generation framework for producing structure-specific models and fragility functions of bridges. The framework effectively fuses geometric and semantic information extracted from Google Street View images with centerline curve geometry, surface topology, and various relevant metadata to construct extremely accurate geometric representations of bridges. Then, using class statistics available in the literature for bridge structural properties, the framework generates structural models. Both the performance of the geometry extraction procedure and the structural modeling method proposed here are validated by comparison against the structural model of a real-life bridge developed based on as-built drawings.In principle, these models can be utilized to assess physical damage for any type of hazard, but in this study, the focus is limited to seismic applications. Thus to relate the damage resulting from seismic demands from ground shaking, bridge-specific fragility functions are developed for 100 bridge structures in the immediate surroundings of Ports of Los Angeles and Long Beach. Using these fragility curves, the physical damage resulting from a magnitude 7.3 scenario earthquake on Palos Verdes fault is predicted. Subsequently, the effects of the bridge infrastructure damage to the transportation patterns in the Los Angeles metropolitan area are investigated in terms of various resilience metrics

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

Symmetry in 3D shapes - analysis and applications to model synthesis

Symmetry is an essential property of a shapes\u27 appearance and presents a source of information for structure-aware deformation and model synthesis. This thesis proposes feature-based methods to detect symmetry and regularity in 3D shapes and demonstrates the utilization of symmetry information for content generation. First, we will introduce two novel feature detection techniques that extract salient keypoints and feature lines for a 3D shape respectively. Further, we will propose a randomized, feature-based approach to detect symmetries and decompose the shape into recurring building blocks. Then, we will present the concept of docking sites that allows us to derive a set of shape operations from an exemplar and will produce similar shapes. This is a key insight of this thesis and opens up a new perspective on inverse procedural modeling. Finally, we will present an interactive, structure-aware deformation technique based entirely on regular patterns.Symmetrie ist eine essentielle Eigenschaft für das Aussehen eines Objekts und bietet eine Informationsquelle für strukturerhaltende Deformation und Modellsynthese. Diese Arbeit beschäftigt sich mit merkmalsbasierter Symmetrieerkennung in 3D-Objekten und der Synthese von 3D-Modellen mittels Symmetrieinformationen. Zunächst stellen wir zwei neue Verfahren zur Merkmalserkennung vor, die hervorstechende Punkte bzw. Linien in 3D-Objekten erkennen. Darauf aufbauend beschreiben wir einen randomisierten, merkmalsbasierten Ansatz zur Symmetrieerkennung, der ein Objekt in sich wiederholende Bausteine zerlegt. Des Weiteren führen wir ein Konzept zur Modifikation von Objekten ein, welches Andockstellen in Geometrie berechnet und zur Generierung von ähnlichen Objekten eingesetzt werden kann. Dieses Konzept eröffnet völlig neue Möglichkeiten für die Ermittlung von prozeduralen Regeln aus Beispielen. Zum Schluss präsentieren wir eine interaktive Technik zur strukturerhaltenden Deformation, welche komplett auf regulären Strukturen basiert