Theoretical part of the thesis is devoted to the relationship of open source licensing to innovation, rapid prototyping and technologies used for 3D printing. There is also a comparison of selected RepRap printers. The main objective is to explore the possibilities and limitations of RepRap 3D printers while also analyzing their use in the Czech market. The aim of the application part is to test selected RepRap printer and explore the possibilities of 3D printing on these devices. In the last part of the thesis research has been conducted on the use of 3D printers. It's objective is to answer the main research question - who and for what purpose uses and aquires 3D printers

L’angioplastie coronarienne est une intervention guidée par rayons X et réalisée par voie endovasculaire qui restaure le diamètre des vaisseaux coronaires réduit par accumulation de corps gras au sein de leurs parois. Lors de cette procédure, un guide est introduit à partir d’une artère qui peut être située au poignet ou au niveau de l’aine. La technique consiste alors à amener dans le vaisseau pathologique un ballonnet gonflable dans la zone rétrécie, localisée grâce à l’injection préalable d’un produit de contraste. Le gonflement du ballonnet élargit l’artère et s’accompagne en général de la pose d’un stent, structure métallique capable de renforcer la paroi. Le geste clinique peut être facilité en intégrant aux images le détail de la nature de la paroi, information disponible avec un scanner 3D. Le but de cette thèse est de proposer une méthode de recalage déformable pour superposer cette information 3D à des images angiographiques 2D per-opératoire en déformant le modèle 3D afin qu’il suive la dynamique cardiaque capturée dans les images angiographiques. Nous introduisons un algorithme de segmentation capable de segmenter automatiquement les vaisseaux principaux dans ces images angiographiques. Ensuite, nous présentons une approche de suivi du vaisseau 3D pathologique dans une séquence 2D+t combinant appariements et déformation d’une courbe spline. Enfin, nous décrivons l’extension au suivi d'un arbre vasculaire 3D, représenté par un arbre dont les arcs sont des courbes splines, dans une séquence 2D+t. Nous avons privilégié les approches applicables avec une seule projection angiographique, bien adapté au déroulement usuel des procédures cliniques. La performance des algorithmes a fait l’objet d’évaluations quantitatives sur des données réelles incluant 30 images pour la segmentation et 23 séquences pour le recalage. / Coronary angioplasty is an X-ray guided intervention, which aims at recovering the diameter of coronary vessels when the accumulation of fat in the vessel wall reduced it. During this procedure, a guide-wire is inserted in the blood vessel located at the wrist or groin. This guide-wire brings into the pathologic vessel a balloon at the level of the fat accumulation, thanks to a previous contrast injection which highlights the lesion. The balloon is inflated and very frequently a thin mesh tube of metallic wires (stent), which is wrapped around the balloon, is then expanded during the balloon inflation. The procedure could benefit from additional information on the nature of the inner wall, available on 3D CT scan. The aim of the thesis is to propose a dynamic registration to superimpose this 3D information onto the intraoperative 2D angiographic sequence, by deforming the 3D model so that it can follow the cardiac motion captured thanks to the angiographic images. We introduce a segmentation algorithm able to automatically segment the main vessels of the angiographic images. Then, we present a tracking approach of the 3D pathologic vessel in a 2D+t sequence combining pairings and the deformation of a spline curve. Finally, we describe the extension to the 3D vascular tree tracking represented by a tree, whose edges are spline curves, in a 2D+t sequence. We favored approaches that are applicable to a single angiographic projection, which is well adapted to the usual process of clinical procedures. All the proposed methods have been tested on real data, consisting of 30 angiographic images for the segmentation algorithm and 23 angiographic sequences for the registration algorithms.

Achieving robot autonomy is an extremely challenging task and it starts with developing algorithms that help the robot understand how humans perceive the environment around them. Once the robot understands how to make sense of its environment, it is easy to make efficient decisions about safe movement. It is hard for robots to perform tasks that come naturally to humans like understanding signboards, classifying traffic lights, planning path around dynamic obstacles, etc. In this work, we take up one such challenge of motion segmentation using Light Detection and Ranging (LiDAR) point clouds. Motion segmentation is the task of classifying a point as either moving or static. As the ego-vehicle moves along the road, it needs to detect moving cars with very high certainty as they are the areas of interest which provide cues to the ego-vehicle to plan it's motion. Motion segmentation algorithms segregate moving cars from static cars to give more importance to dynamic obstacles. In contrast to the usual LiDAR scan representations like range images and regular grid, this work uses a modern representation of LiDAR scans using permutohedral lattices. This representation gives ease of representing unstructured LiDAR points in an efficient lattice structure. We propose a machine learning approach to perform motion segmentation. The network architecture takes in two sequential point clouds and performs convolutions on them to estimate if 3D points from the first point cloud are moving or static. Using two temporal point clouds help the network in learning what features constitute motion. We have trained and tested our learning algorithm on the FlyingThings3D dataset and a modified KITTI dataset with simulated motion.

The essence of this work is to summarize the knowledge about 3D printing, especially with FDM technologies. Create an overview of used FDM printing materials, identify their advantages and disadvantages, technical parameters and usability. Another purpose of the thesis is to examine the print quality and its problems and specify types of surface finishes for selected materials. The content also includes remote control and monitoring of the 3D print process using the Raspberry Pi microcomputer and the print server named Octoprint.

This work is focused on solving the problems of delta 3d printer frame rigidity and impact of rigidity on final quality of 3d printing. A variant of a 3d printer on a classic Kossel-shaped frame has designed and built. Further, frame strength analysis and improvement design is performed, with subsequent production of a new variant and comparison of the 3d printing results of both variants. The result of this work is to show how the frame construction really affects the print quality.

This thesis deals with properties of austenitic stainless steel 304L processed by SLM technology and tested at room and cryogenics temperatures. Result is description of mechanical properties and microstructure. First step was to develop processing parameters to achieve porosity of prints fell below 0,01 %. Following tensile test showed higher yield and ultimate tensile strength than conventionally fabricated parts, even at temperature -80 °C, but at cost of reduced ductility. Due to deformation and low temperature austenite transformed into martensite. This transformation also occurred in Charpy toughness test, that resulted in ductile to brittle behaviour.

This thesis describes reconstruction of scene which is scan trough two cameras. There are described methods of calibration of cameras system, methods for finding the corners and methods for finding correspondences. Corners are searched by FAST detector and for search correspondences are used normalized cross correlation. In the framework of 3D reconstruction is implemented rectification. The final shape is saved to VRML format. In the thesis are described parallelization options. The calculation of the correlation is optimized for multiprocessors CPU and there are designed implementations of algorithm to GPU and FPGA too.

This thesis deals with 3D reconstruction using stereo cameras. This work is to show the usefulness of GPU acceleration for sophisticated algorithm

Master thesis deals with the creation of the data visualization measured by 3D laser scanner using the Point Cloud method. Measured data were parameterized after loading for use in programming environment of Microsoft Visual Studio 2010 and in platform XNA. Individual data points forms the center of defined cubes which are displayed and create a scene where is possible to move through a user input in the form of a keyboard or mouse. Created algorithms were analyzed to determine the total running speed of the program, the individual as well as critical sections. The algorithms were optimized to a higher running speed of the program on the basis of analyzed data. Optimization was focused on the selection of retrieved data and on the method of their saving within the program environment. The next optimization process was based on the using of the other method for displaying of measured data points. Individual data points were displayed in form of square 2D texture replacing the cube. This square is rotating according to move of observer. Designed algorithm optimization leads to faster running of the program.

Thesis solves 3D reconstruction of an object by method of lighting by pattern. A projector lights the measured object by defined pattern and two cameras are measuring 2D points from it. The pedestal of obejct rotates and during the measure are acquired data from different angles. Points are indentified from measured images, transformed to 3D using stereovision, connected to 3D model and displayed.