Shape reconstruction of different three dimensional objects using multiple range images has evolved recently within the recent past. In this research shape reconstruction of a three dimensional object using multiple range image views is investigated. Range images were captured using the Waikato Range Imager. This range images camera is novel in that it uses heterodyne imaging and is capable of acquiring range images with precision less than a millimeter simultaneously over a full field. Multiple views of small objects were taken and the FastRBF was explored as a mean of registration and surface rendering. For comparison to the real range data, simulated range data under noise free condition were also generated and reconstructed with the FastRBF tool box. The registration and reconstruction of simple object was performed using different views with the FastRBF toolbox. Analysis of the registration process showed that the translation error produced due to distortion during registration of different views hinders the process of reconstructing a complete surface. While analyzing the shape reconstruction using the FastRBF tool it is also determined that a small change in accuracy values can affect the interpolation drastically. Results of reconstruction of a real 3D object from multiple views are shown.

This thesis presents a web-based approach to lighting three-dimensional geometry in a virtual scene. The use of High Dynamic Range (HDR) images for the lighting model makes it possible to convey a greater sense of photorealism than can be provided with a conventional computer generated three-point lighting setup. The use of QuickTime ™ Object Virtual Reality to display the three-dimensional geometry offers a sophisticated user experience and a convenient method for viewing virtual objects over the web. With this work, I generate original High Dynamic Range images for the purpose of image-based lighting and use the QuickTime ™ Object Virtual Reality framework to creatively alter the paradigm of object VR for use in object lighting. The result is two scenarios: one that allows for the virtual manipulation of an object within a lit scene, and another with the virtual manipulation of light around a static object. Future work might include the animation of High Dynamic Range image-based lighting, with emphasis on such features as depth of field and glare generation.

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This work describes a method based on three stages for reconstructing a model from a given set of scanned meshes obtained from 3D scanners. Meshes scanned from different scanner s view points have their representation in local coordinate systems. Therefore, for final model reconstruction, an alignment of the meshes is required. The most popular algorithm for cloud data registration is the ICP algorithm. However, ICP requires an initial estimate of mesh alignment, which is, many times, done manually. To automate this process, this work uses a surface representation called spin-images to identify overlap areas between the meshes and to estimate their alignment. After this initial registration, the alignment is refined by the ICP algorithm, and finally the model is reconstructed using a method called VRIP. / Fundação de Amparo a Pesquisa do Estado de Alagoas / Este trabalho descreve um método baseado em três etapas para reconstrução de modelos a partir de malhas capturadas de scanners 3D. Malhas obtidas a partir de diferentes pontos de visão de um scanner têm sua representação em sistemas de coordenadas local. Portanto, para a reconstrução final do modelo, é necessário realizar um alinhamento dessas malhas, ou registro. O algoritmo mais famoso para realizar registro de nuvens de pontos é o algoritmo ICP. Porém, um dos requisitos desse algoritmo é uma estimativa inicial do alinhamento das malhas, que muitas vezes é feita manualmente. Para automatizar esse processo, este trabalho utiliza descritores spin-image para identificar regiões de sobreposição entre as malhas e estimar seus alinhamentos. Após este registro inicial, o alinhamento é refinado através do algoritmo ICP, e finalmente o modelo é reconstruído usando uma técnica chamada VRIP.

Thesis (MSc)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Range images are images that store at each pixel the distance between the sensor and a particular point in the observed scene, instead of the colour information. They provide a convenient storage format for 3-D point cloud information captured from a single point of view. Range image segmentation is the process of grouping the pixels of a range image into regions of points that belong to the same surface. Segmentations are useful for many applications that require higherlevel information, and with range images they also represent a significant step towards complete scene reconstruction. This study considers the segmentation of range images into planar surfaces. It discusses the theory and also implements and evaluates some current approaches found in the literature. The study then develops a new approach based on the theory of graph cut optimization which has been successfully applied to various other image processing tasks but, according to a search of the literature, has otherwise not been used to attempt segmenting range images. This new approach is notable for its strong guarantees in optimizing a specific energy function which has a rigorous theoretical underpinning for handling noise in images. It proves to be very robust to noise and also different values of the few parameters that need to be trained. Results are evaluated in a quantitative manner using a standard evaluation framework and datasets that allow us to compare against various other approaches found in the literature. We find that our approach delivers results that are competitive when compared to the current state-of-the-art, and can easily be applied to images captured with different techniques that present varying noise and processing challenges. / AFRIKAANSE OPSOMMING: Dieptebeelde is beelde wat vir elke piksel die afstand tussen die sensor en ’n spesifieke punt in die waargenome toneel, in plaas van die kleur, stoor. Dit verskaf ’n gerieflike stoorformaat vir 3-D puntwolke wat vanaf ’n enkele sigpunt opgeneem is. Die segmentasie van dieptebeelde is die proses waarby die piksels van ’n dieptebeeld in gebiede opgedeel word, sodat punte saam gegroepeer word as hulle op dieselfde oppervlak lê. Segmentasie is nuttig vir verskeie toepassings wat hoërvlak inligting benodig en, in die geval van dieptebeelde, verteenwoordig dit ’n beduidende stap in die rigting van volledige toneel-rekonstruksie. Hierdie studie ondersoek segmentasie waar dieptebeelde opgedeel word in plat vlakke. Dit bespreek die teorie, en implementeer en evalueer ook sekere van die huidige tegnieke wat in die literatuur gevind kan word. Die studie ontwikkel dan ’n nuwe tegniek wat gebaseer is op die teorie van grafieksnit-optimering wat al suksesvol toegepas is op verskeie ander beeldverwerkingsprobleme maar, sover ’n studie op die literatuur wys, nog nie gebruik is om dieptebeelde te segmenteer nie. Hierdie nuwe benadering is merkbaar vir sy sterk waarborge vir die optimering van ’n spesifieke energie-funksie wat ’n sterk teoretiese fondasie het vir die hantering van geraas in beelde. Die tegniek bewys om fors te wees tot geraas sowel as die keuse van waardes vir die min parameters wat afgerig moet word. Resultate word geëvalueer op ’n kwantitatiewe wyse deur die gebruik van ’n standaard evalueringsraamwerk en datastelle wat ons toelaat om hierdie tegniek te vergelyk met ander tegnieke in die literatuur. Ons vind dat ons tegniek resultate lewer wat mededingend is ten opsigte van die huidige stand-van-die-kuns en dat ons dit maklik kan toepas op beelde wat deur verskeie tegnieke opgeneem is, alhoewel hulle verskillende geraastipes en verwerkingsuitdagings bied.

<p>Visualization is the process of constructing methods, which are able to synthesize interesting and informative images from data sets, to simplify the process of interpreting the data. In this thesis a new approach to construct meteorological visualization methods using neural network technology is described. The methods are trained with examples instead of explicitely designing the appearance of the visualization.</p><p>This approach is exemplified using two applications. In the fist the problem to compute an image of the sky for dynamic weather, that is taking account of the current weather state, is addressed. It is a complicated problem to tie the appearance of the sky to a weather state. The method is trained with weather data sets and images of the sky to be able to synthesize a sky image for arbitrary weather conditions. The method has been trained with various kinds of weather and images data. The results show that this is a possible method to construct weather visaualizations, but more work remains in characterizing the weather state and further refinement is required before the full potential of the method can be explored. This approach would make it possible to synthesize sky images of dynamic weather using a fast and efficient empirical method.</p><p>In the second application the problem of computing synthetic satellite images form numerical forecast data sets is addressed. In this case a mode is trained with preclassified satellite images and forecast data sets to be able to synthesize a satellite image representing arbitrary conditions. The resulting method makes it possible to visualize data sets from numerical weather simulations using synthetic satellite images, but could also be the basis for algorithms based on a preliminary cloud classification.</p> / Report code: LiU-Tek-Lic-2004:66.

It is well established that acquiring large amounts of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and pre-empt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naïve solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape in the context of 3D imaging. This thesis discusses the development of two formal improvement measures, the first based upon surface meshes and Ordinary Kriging that focuses on improving scene accuracy, and the second based upon probabilistic occupancy grids that focuses on improving scene coverage. Furthermore, three selection processes to automatically choose which locations within the field of view of a range sensor to acquire next are proposed based upon the two formal improvement measures. The first two selection processes each use only one of the proposed improvement measures. The third selection process combines both improvement measures in order to counterbalance the parameters of the accuracy of knowledge about the scene and the coverage of the scene. The proposed algorithms mainly target applications using random access range sensors, defined as sensors that can acquire depth measurements at a specified location within their field of view. Additionally, the algorithms are applicable to the case of estimating the improvement and point selection from within a single point of view, with the purpose of guiding the random access sensor to locations it can acquire. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data of several scenes acquired from many different sensors employing various sensing technologies and configurations. Furthermore, the experimental results of the proposed selection processes are compared against those produced by a random sampling process, as well as a neural gas selective sensing algorithm.

Soutenue par les progrès récents et rapides des techniques d'acquisition 3D, la reconnaissance d'objets 3D a suscité de nombreux efforts de recherche durant ces dernières années. Cependant, il reste à résoudre dans ce domaine plusieurs problématiques liées à la grande quantité d'information, à l'invariance à l'échelle et à l'angle de vue, aux occlusions et à la robustesse au bruit.Dans ce contexte, notre objectif est de reconnaitre un objet 3D isolé donné dans une vue requête, à partir d'une base d'apprentissage contenant quelques vues de cet objet. Notre idée est de formuler une méthodologie locale qui combine des aspects d'approches existantes et apporte une amélioration sur la performance de la reconnaissance.Nous avons opté pour une méthode par points d'intérêt (PIs) fondée sur des mesures de la variation locale de la forme. Notre sélection de points saillants est basée sur la combinaison de deux espaces de classification de surfaces : l'espace SC (indice de forme- intensité de courbure), et l'espace HK (courbure moyenne-courbure gaussienne).Dans la phase de description de l'ensemble des points extraits, nous proposons une signature d'histogrammes, qui joint une information sur la relation entre la normale du point référence et les normales des points voisins, avec une information sur les valeurs de l'indice de forme de ce voisinage. Les expérimentations menées ont permis d'évaluer quantitativement la stabilité et la robustesse de ces nouveaux détecteurs et descripteurs.Finalement nous évaluons, sur plusieurs bases publiques d'objets 3D, le taux de reconnaissance atteint par notre méthode, qui montre des performances supérieures aux techniques existantes. / There has been strong research interest in 3D object recognition over the last decade, due to the promising reliability of the 3D acquisition techniques. 3D recognition, however, conveys several issues related to the amount of information, to scales and viewpoints variation, to occlusions and to noise.In this context, our objective is to recognize an isolated object given in a request view, from a training database containing some views of this object. Our idea is to propose a local method that combines some existent approaches in order to improve recognition performance.We opted for an interest points (IPs) method based on local shape variation measures. Our selection of salient points is done by the combination of two surface classification spaces: the SC space (Shape Index-Curvedness), and the HK space (Mean curvature- Gaussian curvature).In description phase of the extracted set of points, we propose a histogram based signature, in which we join information about the relationship between the reference point normal and normals of its neighbors, with information about the shape index values of this neighborhood. Performed experiments allowed us to evaluate quantitatively the stability and the robustness of the new proposed detectors and descriptors.Finally we evaluate, on several public 3D objects databases, the recognition rate attained by our method, which outperforms existing techniques on same databases.

It is well established that acquiring large amounts of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and pre-empt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naïve solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape in the context of 3D imaging. This thesis discusses the development of two formal improvement measures, the first based upon surface meshes and Ordinary Kriging that focuses on improving scene accuracy, and the second based upon probabilistic occupancy grids that focuses on improving scene coverage. Furthermore, three selection processes to automatically choose which locations within the field of view of a range sensor to acquire next are proposed based upon the two formal improvement measures. The first two selection processes each use only one of the proposed improvement measures. The third selection process combines both improvement measures in order to counterbalance the parameters of the accuracy of knowledge about the scene and the coverage of the scene. The proposed algorithms mainly target applications using random access range sensors, defined as sensors that can acquire depth measurements at a specified location within their field of view. Additionally, the algorithms are applicable to the case of estimating the improvement and point selection from within a single point of view, with the purpose of guiding the random access sensor to locations it can acquire. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data of several scenes acquired from many different sensors employing various sensing technologies and configurations. Furthermore, the experimental results of the proposed selection processes are compared against those produced by a random sampling process, as well as a neural gas selective sensing algorithm.

No description available.