Title: Analysis of Point Clouds Representing Surfaces of Engineering Practice Author: Petra Surynková Department: Department of Mathematics Education Supervisor: Mgr. Šárka Voráčová, Ph.D., Faculty of Transportation Sciences, Czech Technical University in Prague Abstract: The doctoral dissertation Analysis of Point Clouds Representing Surfaces of Engineering Practice addresses the development and application of methods of digital reconstruction of surfaces of engineering and construction practice from point clouds. The main outcome of the dissertation is a presentation of new procedures and methods that contribute to each of the stages of the reconstruction process from the input point clouds. The work is mainly focused on the analysis of input clouds that describe special types of surfaces. Several completely new algorithms and improvements of existing algorithms that contribute to individual steps of surface reconstruction are presented. New procedures are based on geometrical characteristics of the reconstructed object. An important result of the dissertation is an analysis of not only synthetically generated point clouds but above all an analysis of real point clouds that have been obtained from measurements of real objects. The significant contribution of the dissertation is also an...

Im Rahmen einer Diplomarbeit an der TU Bergakademie Freiberg wurden in 2014 die Grundlagen für die Auswertung von 3D-Punktwolken zur automatisierten Nachtragung des Risswerks gelegt. Um die dafür notwendigen 3D-Punktwolken möglichst wirtschaftlich zu erstellen, laufen seit 2015 Untersuchungen und Testmessungen zur Machbarkeit des untertägigen Einsatzes von mobil messenden Laserscannern. Im Folgenden werden verschiedene technische Ansätze sowie die Ergebnisse der Testmessungen und die weiteren geplanten Schritte vorgestellt. / As part of a thesis at the Technical University of Freiberg, a basis for the analysis of 3D point clouds was set for refining the mine map automatically. Since 2015 studies and test measurements have been running to create the necessary 3D point clouds as economically as possible, by using an underground mobile scanning system. Below the different technical approaches will be presented as well as the results of the test measurements and the next planned steps.

Cette thèse propose des modèles cartographiques à grande échelle d'environnements urbains et ruraux à l'aide de données en 3D acquises par plusieurs robots. La mémoire contribue de deux manières principales au domaine de recherche de la cartographie. La première contribution est la création d'une nouvelle structure, CoMapping, qui permet de générer des cartes 3D de façon collaborative. Cette structure s’applique aux environnements extérieurs en ayant une approche décentralisée. La fonctionnalité de CoMapping comprend les éléments suivants : Tout d’abord, chaque robot réalise la construction d'une carte de son environnement sous forme de nuage de points.Pour cela, le système de cartographie a été mis en place sur des ordinateurs dédiés à chaque voiture, en traitant les mesures de distance à partir d'un LiDAR 3D se déplaçant en six degrés de liberté (6-DOF). Ensuite, les robots partagent leurs cartes locales et fusionnent individuellement les nuages de points afin d'améliorer leur estimation de leur cartographie locale. La deuxième contribution clé est le groupe de métriques qui permettent d'analyser les processus de fusion et de partage de cartes entre les robots. Nous présentons des résultats expérimentaux en vue de valider la structure CoMapping et ses métriques. Tous les tests ont été réalisés dans des environnements extérieurs urbains du campus de l’École Centrale de Nantes ainsi que dans des milieux ruraux. / This thesis proposes large-scale mapping model of urban and rural environments using 3D data acquired by several robots. The work contributes in two main ways to the research field of mapping. The first contribution is the creation of a new framework, CoMapping, which allows to generate 3D maps in a cooperative way. This framework applies to outdoor environments with a decentralized approach. The CoMapping's functionality includes the following elements: First of all, each robot builds a map of its environment in point cloud format.To do this, the mapping system was set up on computers dedicated to each vehicle, processing distance measurements from a 3D LiDAR moving in six degrees of freedom (6-DOF). Then, the robots share their local maps and merge the point clouds individually to improve their local map estimation. The second key contribution is the group of metrics that allow to analyze the merging and card sharing processes between robots. We present experimental results to validate the CoMapping framework with their respective metrics. All tests were carried out in urban outdoor environments on the surrounding campus of the École Centrale de Nantes as well as in rural areas.

Ziel dieser Masterarbeit ist die Untersuchung neuartiger Prägeformkonzepte hinsichtlich ihres Prägeverhaltens. Die Konzepte weisen lokal unterschiedliche Materialeigenschaften auf, die zu einer verbesserten Ausprägung von Karton führen sollen. Die Konzepte sollen anhand der Prägeergebnisse und der Abformgenauigkeit evaluiert werden. Dabei ist ein weiteres Ziel der Arbeit, Methoden zur Quantifizierung der Abformgenauigkeit zu finden. Die Herstellung der Konzepte erfolgt mithilfe eines additiven Fertigungssystems, welches mehrere Materialien in einem Bauvorgang verarbeiten kann. Zur Datengewinnung werden Oberflächenscans der geprägten Kartonproben und Werkzeuge durchgeführt. Auf Grundlage dieser Scans werden drei Methoden zur Ermittlung der Abformgenauigkeit vorgeschlagen. Abschließend werden die Werkzeuge anhand der Prägeergebnisse und der ermittelten Abformgenauigkeit bewertet. Weiterhin werden die vorgeschlagenen Methoden miteinander verglichen und deren Vor- und Nachteile diskutiert. Dies gibt Auskunft darüber, unter welchen Bedingungen der Einsatz welcher Methode sinnvoll erscheint.:1. Einleitung 2. Theoretische Grundlagen 2.1 Umformprozesse 2.1.1 Prägen von Faserwerkstoffen 2.1.2 Einsatz von Niederhaltern beim Umformen von Blechen 2.1.2.1 Tiefenziehen 2.1.2.2 Tiefen 2.1.3 Einsatz von Niederhaltern beim Umformen von Karton 2.1.3.1 Ziehen und Pressformen 2.1.3.2 Hydroformen 2.2 Multi-Material-Verarbeitung in der additiven Fertigung 2.2.1 Materialextrusion 2.2.2 Badbasierte Photopolymerisation 2.2.3 Material Jetting 2.2.4 Pulverbettbasiertes Schmelzen 2.2.5 Workflow und Datenvorbereitung 2.3.6 Geeignete Dateiformate 2.3 Soll-Ist-Vergleich von 2.5D-Oberflächendaten 2.3.1 Berechnung von Flächeninhalten und Volumen 2.3.2 Registrierung und Abstandsberechnung von Punktwolken 3. Versuche und Messungen 3.1 Herstellung der Prägeformkonzepte 3.1.1 Beschreibung der Konzepte 3.1.2 Fertigungstechnologie und Materialwahl 3.1.3 Datenvorbereitung für die Polyjet-Fertigung 3.2 Prägeversuche und Datenverarbeitung 3.2.1 Prägeversuche 3.2.2 Oberflächenscan am Keyence 3D-Makroskop 3.3 Ermittlung der Abformgenauigkeit 3.3.1 Flächen- und Volumenberechnung in MatLab & CloudCompare 3.3.2 Abformgenauigkeit nach ICP-Algorithmus & Abstandsberechnung 4. Ergebnisse und Diskussion 4.1 Betrachtung der Prägewerkzeuge 4.2 Betrachtung der Kartonprägungen 4.2.1 Prägeergebnisse nach Flächeninhalt der Profilschnitte 4.2.2 Einfluss der Faserlaufrichtung auf Kartonprägungen 4.2.3 Prägeergebnisse nach Volumen der Punktwolken 4.3 Betrachtung der Abformgenauigkeit 4.3.1 Abformgenauigkeit nach Flächeninhalt & Volumen 4.3.2 Abformgenauigkeit nach ICP-Algorithmus & Abstandsberechnung 4.4 Bewertung der Methoden zur Ermittlung der Abformgenauigkeit 4.5 Beurteilung des Bedienereinflusses bei der Datenverarbeitung am Keyence 5. Zusammenfassung und Ausblick Literaturverzeichnis Eidesstattliche Erklärung / The aim of this master thesis is to investigate novel embossing die concepts with regard to their embossing behavior. The concepts have locally different material properties, which should lead to an improved embossing of cardboard. The concepts are to be evaluated on the basis of the embossing results and the impression accuracy. A further aim of the work is to find methods for quantifying the impression accuracy. The concepts will be manufactured using an additive manufacturing system that can process multiple materials in a single build process. Surface scans of the embossed cardboard samples and tools are performed to obtain data. Based on these scans, three methods are proposed to determine the impression accuracy. Finally, the tools are evaluated based on the embossing results and the determined impression accuracy. Furthermore, the proposed methods are compared with each other and their advantages and disadvantages are discussed. This provides information on the conditions under which the use of which method appears to be sensible.:1. Einleitung 2. Theoretische Grundlagen 2.1 Umformprozesse 2.1.1 Prägen von Faserwerkstoffen 2.1.2 Einsatz von Niederhaltern beim Umformen von Blechen 2.1.2.1 Tiefenziehen 2.1.2.2 Tiefen 2.1.3 Einsatz von Niederhaltern beim Umformen von Karton 2.1.3.1 Ziehen und Pressformen 2.1.3.2 Hydroformen 2.2 Multi-Material-Verarbeitung in der additiven Fertigung 2.2.1 Materialextrusion 2.2.2 Badbasierte Photopolymerisation 2.2.3 Material Jetting 2.2.4 Pulverbettbasiertes Schmelzen 2.2.5 Workflow und Datenvorbereitung 2.3.6 Geeignete Dateiformate 2.3 Soll-Ist-Vergleich von 2.5D-Oberflächendaten 2.3.1 Berechnung von Flächeninhalten und Volumen 2.3.2 Registrierung und Abstandsberechnung von Punktwolken 3. Versuche und Messungen 3.1 Herstellung der Prägeformkonzepte 3.1.1 Beschreibung der Konzepte 3.1.2 Fertigungstechnologie und Materialwahl 3.1.3 Datenvorbereitung für die Polyjet-Fertigung 3.2 Prägeversuche und Datenverarbeitung 3.2.1 Prägeversuche 3.2.2 Oberflächenscan am Keyence 3D-Makroskop 3.3 Ermittlung der Abformgenauigkeit 3.3.1 Flächen- und Volumenberechnung in MatLab & CloudCompare 3.3.2 Abformgenauigkeit nach ICP-Algorithmus & Abstandsberechnung 4. Ergebnisse und Diskussion 4.1 Betrachtung der Prägewerkzeuge 4.2 Betrachtung der Kartonprägungen 4.2.1 Prägeergebnisse nach Flächeninhalt der Profilschnitte 4.2.2 Einfluss der Faserlaufrichtung auf Kartonprägungen 4.2.3 Prägeergebnisse nach Volumen der Punktwolken 4.3 Betrachtung der Abformgenauigkeit 4.3.1 Abformgenauigkeit nach Flächeninhalt & Volumen 4.3.2 Abformgenauigkeit nach ICP-Algorithmus & Abstandsberechnung 4.4 Bewertung der Methoden zur Ermittlung der Abformgenauigkeit 4.5 Beurteilung des Bedienereinflusses bei der Datenverarbeitung am Keyence 5. Zusammenfassung und Ausblick Literaturverzeichnis Eidesstattliche Erklärung

In computer vision, it has in recent years become more popular to use point clouds to represent 3D data. To understand what a point cloud contains, methods like semantic segmentation can be used. Semantic segmentation is the problem of segmenting images or point clouds and understanding what the different segments are. An application for semantic segmentation of point clouds are e.g. autonomous driving, where the car needs information about objects in its surrounding. Our approach to the problem, is to project the point clouds into 2D virtual images using the Katz projection. Then we use pre-trained convolutional neural networks to semantically segment the images. To get the semantically segmented point clouds, we project back the scores from the segmentation into the point cloud. Our approach is evaluated on the semantic3D dataset. We find our method is comparable to state-of-the-art, without any fine-tuning on the Semantic3Ddataset.

Im Rahmen einer Diplomarbeit an der TU Bergakademie Freiberg wurden in 2014 die Grundlagen für die Auswertung von 3D-Punktwolken zur automatisierten Nachtragung des Risswerks gelegt. Um die dafür notwendigen 3D-Punktwolken möglichst wirtschaftlich zu erstellen, laufen seit 2015 Untersuchungen und Testmessungen zur Machbarkeit des untertägigen Einsatzes von mobil messenden Laserscannern. Im Folgenden werden verschiedene technische Ansätze sowie die Ergebnisse der Testmessungen und die weiteren geplanten Schritte vorgestellt. / As part of a thesis at the Technical University of Freiberg, a basis for the analysis of 3D point clouds was set for refining the mine map automatically. Since 2015 studies and test measurements have been running to create the necessary 3D point clouds as economically as possible, by using an underground mobile scanning system. Below the different technical approaches will be presented as well as the results of the test measurements and the next planned steps.

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Discrimination of different tree crowns based on their 3D shapes is essential for a wide range of forestry applications, and, due to its complexity, is a significant challenge. This study presents a modified 3D shape descriptor for the perception of different tree crown shapes in discrete-return LiDAR point clouds. The proposed methodology comprises of five main components, including definition of a local coordinate system, learning salient points, generation of simulated LiDAR point clouds with geometrical shapes, shape signature generation (from simulated LiDAR points as reference shape signature and actual LiDAR point clouds as evaluated shape signature), and finally, similarity assessment of shape signatures in order to extract the shape of a real tree. The first component represents a proposed strategy to define a local coordinate system relating to each tree to normalize 3D point clouds. In the second component, a learning approach is used to categorize all 3D point clouds into two ranks to identify interesting or salient points on each tree. The third component discusses generation of simulated LiDAR point clouds for two geometrical shapes, including a hemisphere and a half-ellipsoid. Then, the operator extracts 3D LiDAR point clouds of actual trees, either deciduous or evergreen. In the fourth component, a longitude-latitude transformation is applied to simulated and actual LiDAR point clouds to generate 3D shape signatures of tree crowns. A critical step is transformation of LiDAR points from their exact positions to their longitude and latitude positions using the longitude-latitude transformation, which is different from the geographic longitude and latitude coordinates, and labeled by their pre-assigned ranks. Then, natural neighbor interpolation converts the point maps to raster datasets. The generated shape signatures from simulated and actual LiDAR points are called reference and evaluated shape signatures, respectively. Lastly, the fifth component determines the similarity between evaluated and reference shape signatures to extract the shape of each examined tree. The entire process is automated by ArcGIS toolboxes through Python programming for further evaluation using more tree crowns in different study areas. Results from LiDAR points captured for 43 trees in the City of Surrey, British Columbia (Canada) suggest that the modified shape descriptor is a promising method for separating different shapes of tree crowns using LiDAR point cloud data. Experimental results also indicate that the modified longitude-latitude shape descriptor fulfills all desired properties of a suitable shape descriptor proposed in computer science along with leaf-off, leaf-on invariance, which makes this process autonomous from the acquisition date of LiDAR data. In summary, the modified longitude-latitude shape descriptor is a promising method for discriminating different shapes of tree crowns using LiDAR point cloud data.

Yes / Recovering high-quality 3D point clouds from monocular endoscopic images is a challenging task. This paper proposes a novel deep learning-based computational framework for 3D point cloud reconstruction from single monocular endoscopic images. An unsupervised mono-depth learning network is used to generate depth information from monocular images. Given a single mono endoscopic image, the network is capable of depicting a depth map. The depth map is then used to recover a dense 3D point cloud. A generative Endo-AE network based on an auto-encoder is trained to repair defects of the dense point cloud by generating the best representation from the incomplete data. The performance of the proposed framework is evaluated against state-of-the-art learning-based methods. The results are also compared with non-learning based stereo 3D reconstruction algorithms. Our proposed methods outperform both the state-of-the-art learning-based and non-learning based methods for 3D point cloud reconstruction. The Endo-AE model for point cloud completion can generate high-quality, dense 3D endoscopic point clouds from incomplete point clouds with holes. Our framework is able to recover complete 3D point clouds with the missing rate of information up to 60%. Five large medical in-vivo databases of 3D point clouds of real endoscopic scenes have been generated and two synthetic 3D medical datasets are created. We have made these datasets publicly available for researchers free of charge. The proposed computational framework can produce high-quality and dense 3D point clouds from single mono-endoscopy images for augmented reality, virtual reality and other computer-mediated medical applications.

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\'s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\'s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.