CAD-model-based vision for space applications

A pose acquisition system operating in space must be able to perform well in a variety of different applications including automated guidance and inspections tasks with many different, but known objects. Since the space station is being designed with automation in mind, there will be CAD models of all the objects, including the station itself. The construction of vision models and procedures directly from the CAD models is the goal of this project. The system that is being designed and implementing must convert CAD models to vision models, predict visible features from a given view point from the vision models, construct view classes representing views of the objects, and use the view class model thus derived to rapidly determine the pose of the object from single images and/or stereo pairs

Modeling 3D animals from a side-view sketch

Shape Modeling International 2014International audienceUsing 2D contour sketches as input is an attractive solution for easing the creation of 3D models. This paper tackles the problem of creating 3D models of animals from a single, side-view sketch. We use the a priori assumptions of smoothness and structural symmetry of the animal about the sagittal plane to inform the 3D reconstruction. Our contributions include methods for identifying and inferring the contours of shape parts from the input sketch, a method for identifying the hierarchy of these structural parts including the detection of approximate symmetric pairs, and a hierarchical algorithm for positioning and blending these parts into a consistent 3D implicit-surface-based model. We validate this pipeline by showing that a number of plausible animal shapes can be automatically constructed from a single sketch

3D object reconstruction from line drawings.

Cao Liangliang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 64-69).Abstracts in English and Chinese.Chapter 1 --- Introduction and Related Work --- p.1Chapter 1.1 --- Reconstruction from Single Line Drawings and the Applications --- p.1Chapter 1.2 --- Optimization-based Reconstruction --- p.2Chapter 1.3 --- Other Reconstruction Methods --- p.2Chapter 1.3.1 --- Line Labeling and Algebraic Methods --- p.2Chapter 1.3.2 --- CAD Reconstruction --- p.3Chapter 1.3.3 --- Modelling from Images --- p.3Chapter 1.4 --- Finding Faces of Line Drawings --- p.4Chapter 1.5 --- Generalized Cylinder --- p.4Chapter 1.6 --- Research Problems and Our Contribution --- p.5Chapter 1.6.1 --- A New Criteria --- p.5Chapter 1.6.2 --- Recover Objects from Line Drawings without Hidden Lines --- p.6Chapter 1.6.3 --- Reconstruction of Curved Objects --- p.6Chapter 1.6.4 --- Planar Limbs Assumption and the Derived Models --- p.6Chapter 2 --- A New Criteria for Reconstruction --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Human Visual Perception and the Symmetry Measure --- p.10Chapter 2.3 --- Reconstruction Based on Symmetry and Planarity --- p.11Chapter 2.3.1 --- Finding Faces --- p.11Chapter 2.3.2 --- Constraint of Planarity --- p.11Chapter 2.3.3 --- Objective Function --- p.12Chapter 2.3.4 --- Reconstruction Algorithm --- p.13Chapter 2.4 --- Experimental Results --- p.13Chapter 2.5 --- Summary --- p.18Chapter 3 --- Line Drawings without Hidden Lines: Inference and Reconstruction --- p.19Chapter 3.1 --- Introduction --- p.19Chapter 3.2 --- Terminology --- p.20Chapter 3.3 --- Theoretical Inference of the Hidden Topological Structure --- p.21Chapter 3.3.1 --- Assumptions --- p.21Chapter 3.3.2 --- Finding the Degrees and Ranks --- p.22Chapter 3.3.3 --- Constraints for the Inference --- p.23Chapter 3.4 --- An Algorithm to Recover the Hidden Topological Structure --- p.25Chapter 3.4.1 --- Outline of the Algorithm --- p.26Chapter 3.4.2 --- Constructing the Initial Hidden Structure --- p.26Chapter 3.4.3 --- Reducing Initial Hidden Structure --- p.27Chapter 3.4.4 --- Selecting the Most Plausible Structure --- p.28Chapter 3.5 --- Reconstruction of 3D Objects --- p.29Chapter 3.6 --- Experimental Results --- p.32Chapter 3.7 --- Summary --- p.32Chapter 4 --- Curved Objects Reconstruction from 2D Line Drawings --- p.35Chapter 4.1 --- Introduction --- p.35Chapter 4.2 --- Related Work --- p.36Chapter 4.2.1 --- Face Identification --- p.36Chapter 4.2.2 --- 3D Reconstruction of planar objects --- p.37Chapter 4.3 --- Reconstruction of Curved Objects --- p.37Chapter 4.3.1 --- Transformation of Line Drawings --- p.37Chapter 4.3.2 --- Finding 3D Bezier Curves --- p.39Chapter 4.3.3 --- Bezier Surface Patches and Boundaries --- p.40Chapter 4.3.4 --- Generating Bezier Surface Patches --- p.41Chapter 4.4 --- Results --- p.43Chapter 4.5 --- Summary --- p.45Chapter 5 --- Planar Limbs and Degen Generalized Cylinders --- p.47Chapter 5.1 --- Introduction --- p.47Chapter 5.2 --- Planar Limbs and View Directions --- p.49Chapter 5.3 --- DGCs in Homogeneous Coordinates --- p.53Chapter 5.3.1 --- Homogeneous Coordinates --- p.53Chapter 5.3.2 --- Degen Surfaces --- p.54Chapter 5.3.3 --- DGCs --- p.54Chapter 5.4 --- Properties of DGCs --- p.56Chapter 5.5 --- Potential Applications --- p.59Chapter 5.5.1 --- Recovery of DGC Descriptions --- p.59Chapter 5.5.2 --- Deformable DGCs --- p.60Chapter 5.6 --- Summary --- p.61Chapter 6 --- Conclusion and Future Work --- p.62Bibliography --- p.6

Deformable model to recover circular generalized cylinders

This paper describes a new approach to recover Circular Generalized Cylinders (CGC) using deformable models . This class includes many objects present on industrial site (pipes) or on the natural environment (human leg, tree trunk) . First, we propose a modeling algorithm of objects with constant cross-section radius, called Uniform Circular Generalized Cylinder s (UCGC). With this assumption, reconstruction is possible from a single view of the object. In the case of a single image, the cross - section radius cannot be estimated . If this radius is unknown, reconstruction is achieved up to a scale factor. The model 3D axis i s parametrized by a B-spline curve . After a coarse initialization, the model changes shape to fit the object contour detected in the studied image. Using different views of the object, the previous approach is adapted to Circular Generalized Cylinders (cross-section radiu s variations are now permitted) . No assumption is made on the axis geometry nor on the way the radius varies . In order to be sufficiently adaptative to the large range of object shape belonging to this class, we propose using two independent B-splin e functions to model respectively axis and cross-section radii variations . These algorithms use the geometrical properties of the occluding contours given by the perspective projection of the objects. It i s the first attempt to solve this problem by taking into account this accurate projective model .Dans cet article, nous proposons une méthode de reconstruction des cylindres généralisés à section circulaire par modèle déformable. Tout d'abord, nous formulons l'hypothèse que les sections de l'objet sont de rayon constant. Nous parlons alors de Cylindres Généralisés Circulaires Uniformes (CGCU). Si le rayon est connu a priori, cette hypothèse permet de retrouver l'axe 3D de l'objet, paramétré par une fonction B-Spline, à partir d'une seule vue, sinon la reconstruction est faite à un facteur d'échelle près. Après une initilisation grossière, le modèle est déformé itérativement jusqu'à ce que sa forme devienne cohérente avec les contours extraits de l'image. Nous montrerons ensuite que l'exploitation de différentes vues d'un même objet permet d'adapter notre approche à la reconstruction de Cylindres Généralisés Circulaires (CGC), objets constitués de sections circulaires à rayon non-constant. Aucune hypothèse a priori n'est faite sur la géométrie de l'axe ou sur la façon dont varie le rayon des sections. Afin de pouvoir s'adapter au plus grand nombre d'objets appartenant à cette classe, deux fonctions B-Spline indépendantes sont utilisées pour paramétrer l'axe et la fonction de variation du rayon

Eighth DOD/NASA/FAA Conference on Fibrous Composites in Structural Design, Part 2

Papers presented at the conference are compiled. The conference provided a forum for the scientific community to exchange composite structures design information and an opportunity to observe recent progress in composite structures design and technology. Part 2 contains papers related to the following subject areas: the application in design; methodology in design; and reliability in design

Efficient sketch-based 3D character modelling.

Sketch-based modelling (SBM) has undergone substantial research over the past two decades. In the early days, researchers aimed at developing techniques useful for modelling of architectural and mechanical models through sketching. With the advancement of technology used in designing visual effects for film, TV and games, the demand for highly realistic 3D character models has skyrocketed. To allow artists to create 3D character models quickly, researchers have proposed several techniques for efficient character modelling from sketched feature curves. Moreover several research groups have developed 3D shape databases to retrieve 3D models from sketched inputs. Unfortunately, the current state of the art in sketch-based organic modelling (3D character modelling) contains a lot of gaps and limitations. To bridge the gaps and improve the current sketch-based modelling techniques, this research aims to develop an approach allowing direct and interactive modelling of 3D characters from sketched feature curves, and also make use of 3D shape databases to guide the artist to create his / her desired models. The research involved finding a fusion between 3D shape retrieval, shape manipulation, and shape reconstruction / generation techniques backed by an extensive literature review, experimentation and results. The outcome of this research involved devising a novel and improved technique for sketch-based modelling, the creation of a software interface that allows the artist to quickly and easily create realistic 3D character models with comparatively less effort and learning. The proposed research work provides the tools to draw 3D shape primitives and manipulate them using simple gestures which leads to a better modelling experience than the existing state of the art SBM systems

Functional and Behavioral Implications of Vertebral Structure in Pachyaena ossifraga (Mammalia, Mesonychia)

289-319http://deepblue.lib.umich.edu/bitstream/2027.42/48548/2/ID402.pd

On the origins and evolution of morphological complexity : a developmental perspective

The complexity of organisms has astonished biologists for centuries. How complexity has evolved, has given rise to much debate. Many have claimed that natural selection is the main factor that made it possible to achieve high degrees of complexity. On the contrary, others argue that this is far from the truth, as complexity can increase passively, without the need of natural selection. Either way, the exact mechanisms by which complexity has increased in some groups of organisms remains largely unknown. For morphology, to understand which mechanisms have enabled an increase in complexity, requires to study development. As development is the process that establishes morphology, any evolutionary change in morphology is preceded by a change in its development. Additionally, to understand how morphological complexity evolves, it is necessary to comprehend the phenotypic variation that different developmental mechanisms can produce. The two main questions that I am interested to answer in this dissertation are:  1. Are there some logical requirements that developmental mechanisms should fulfill in order to lead to complex morphologies? 2. How does morphological complexity affect evolution?   To tackle these questions, I used a general computational model of development, EmbryoMaker. EmbryoMaker is a general model that allows to simulate the development of 3D morphologies of any type. It is precisely this generality of EmbryoMaker which was vital for this dissertation, since it allowed an unconstrained exploration of developmental mechanisms, without being limited to certain organisms or systems. This allowed me to tackle the questions I posed in a general way. The general results obtained are: 1. The development of complex morphologies does not require cell signaling or complex gene networks. 2. Extracellular signaling enhances robustness through the compartmentalization of the embryo into different regions of gene expression 3. Complex morphologies are rare 4. The more complex a morphology is, the more finely tuned its developmental parameters need to be 5. The more complex the morphology, the larger the mutational asymmetry towards simplicity 6. The more complex morphology, the more complex the GPM These results indicate that there are qualitative differences in the way complex and simpler morphologies evolve. Complex morphologies evolve under a complex GPM and higher developmental instability. Additionally, complex morphologies produce a higher morphological diversity than simpler morphologies for the same amount of genetic variation, therefore offspring of complex individuals spread across large regions of the morphospace. Finally, these results also indicate that the evolution of morphological complexity becomes progressively slower as complexity increases, until possibly arriving at a complexity trap, where it cannot effectively increase.Luonnosta löytyvien muotojen moninaisuus on ällistyttänyt tutkijoita vuosisatoja ja muotojen kompleksisuuden synnystä on väitelty paljon. Yhtäältä on väitetty luonnonvalinnan olevan ensisijainen eliöiden muodon kompleksisuutta ajava tekijä ja toisaalta on ehdotettu kompleksisuuden voivan kehittyä passiivisesti luonnonvalinnasta riippumatta. Muodon monimutkaistumisen taustalla vaikuttavat mekanismit ovat pitkälti tuntemattomia. Kompleksisuuden lisääntymisen ymmärtäminen edellyttää ymmärrystä yksilönkehityksestä; muodot syntyvät yksilönkehityksen aikana, joten evolutiivinen muutos muodossa edellyttää muutoksia yksilönkehityksessä. Ymmärtääksemme, miten kompleksisuus lisääntyy evoluution myötä, on ymmärrettävä millaista ilmiasujen muuntelua yksilönkehitys voi tuottaa. Kaksi väitöskirjassani käsiteltävää pääkysymystä ovat: Vaaditaanko kehitysmekanismeilta tiettyjä ominaisuuksia, jotta ne voivat tuottaa kompleksisia muotoja? Miten muotojen kompleksisuus vaikuttaa evoluutioon? Käytän työssäni tietokonemallia, EmbryoMakeria, joka mahdollistaa minkä tahansa muodon kehityksen mallinnuksen kolmiuloitteisesti. EmbryoMakerin simulaatiot eivät rajoitu tiettyihin mallieliöihin tai järjestelmiin, mikä on olennaista tutkimukselleni. Tutkimukseni päätulokset ovat: Kompleksisten muotojen kehitys ei edellytä soluviestintää tai monimutkaisia geeniverkostoja. Solujen välinen viestintä lisää kehitysmekanismin vakautta jakamalla alkion rajattuihin geenien ilmentymisalueisiin. Kompleksiset muodot ovat harvinaisia. Mitä kompleksisempi muoto on, sitä tarkemmin sen kehitystä on säädeltävä. Mitä kompleksisempi muoto on, sitä todennäköisemmin mutaatiot johtavat muodon yksinkertaistumiseen. Mitä kompleksisempi muoto on, sitä kompleksisempi on sen taustalla toimiva geeni-ilmiasu-kartta. Tulokseni viittaavat laadullisiin eroihin kompleksisten ja yksinkertaisten muotojen evoluution välillä. Kompleksisten muotojen evoluution taustalla vaikuttava geeni-ilmiasukartta on monimutkaisempi kuin yksinkertaisilla muodoilla. Lisäksi kompleksisten muotojen kehitys on epävakaampaa kuin yksinkertaisten muotojen kehitys. Yksinkertaisiin muotoihin verrattuna kompleksiset muodot johtavat suurempaan monimuotoisuuteen vaikka geneettinen muuntelu taustalla olisi yhtä suurta; tämän vuoksi kompleksisten yksilöiden jälkeläiset levittäytyvät laajoille alueille muotoavaruudessa. Tulokseni osoittavat myös, että kompleksisuuden lisääntyessä kompleksisuuden evoluutio hidastuu, kunnes saavutetaan ’kompleksisuusansa' jossa kompleksisuus ei voi enää lisääntyä