The creation of 3D models of bone from CT images has become popular for surgical planning, the design of implants, and educational purposes. Software is available to convert CT images into 3D models of bone, however, these can be expensive and technically taxing. The goal of this project was to create an open-source and easy-to-use methodology to create 3D models of bone and allow the user to interact with the model to extract desired regions. The method was first created in MATLAB and ported to Python. The CT images were imported into Python and the images were then binarized using a desired threshold determined by the user and based on Hounsfield Units (HU). A Canny edge detector was applied to the binarized images, this extracted the inner and outer surfaces of the bone. Edge points were assigned x, y, and z coordinates based on their pixel location, and the location of the slice in the stack of CT images to create a 3D point cloud. The application of a Delaunay tetrahedralization created a mesh object, the surface was extracted and saved as an STL file. An add-on in Blender was created to allow the user to select the CT images to import, set a threshold, create a 3D mesh model, draw an ROI on the model, and extract that region based on the desired thickness and create a new 3D object. The method was fully open-sourced so was inexpensive and was able to create models of a skull and allow the segmentation of portions of that mesh to create new objects. Future work needs to be conducted to improve the quality of the mesh, implement sampling to reduce the time to create the mesh, and add features that would benefit the end-user. / Thesis / Master of Applied Science (MASc) / The creation of 3D models of bone from CT images has become popular for education, surgical planning, and the design of implants. Software is available to convert CT images into 3D models but can be expensive and technically taxing. The purpose of this project was to develop a process to allow surgeons to create and interact with models from imaging data. This project applied a threshold to binarize a set of CT images, extracted the edges using a Canny Edge detector, and used the edge pixels to create a 3D point cloud. The 3D point cloud was then converted to a mesh object. A user interface was implemented that allowed the selection of portions of the model and a new 3D model to be created from the selection. The process can be improved by improving the quality of the mesh output and adding features to the user interface.

Obstacle detection and avoidance plays a crucial role in the autonomous navigation of unmanned ground vehicles (UGV). Information about the obstacles decreases as the distance between the UGV and obstacles increases. However, this information decreases much more rapidly for negative obstacles than for positive obstacles. UGV navigation becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., potholes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road environments avoiding negative obstacles in all situations is not possible. In such cases, the local path planner may need to choose a candidate path with a negative obstacle that causes the least amount of damage to the vehicle. To deal better with these types of scenarios, this research introduces a novel approach to perform 3D shape estimation of negative obstacles using LiDAR point cloud data. The dimensions (width, diameter, and depth), location (center), and curvature of negative obstacles were calculated based on an estimated shape. The presented approach can estimate the shape of different kinds of negative obstacles such as holes, trenches, in addition to large and complicated negative obstacles. This approach was tested on different terrain types using the Mississippi Autonomous Vehicle Simulation (MAVS).

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Object-centric geometric perception aims at extracting the geometric attributes of 3D objects. These attributes include shape, pose, and motion of the target objects, which enable fine-grained object-level understanding for various tasks in graphics, computer vision, and robotics. With the growth of 3D geometry data and 3D deep learning methods, it becomes more and more likely to achieve such tasks directly using 3D input data. Among different 3D representations, a 3D point cloud is a simple, common, and memory-efficient representation that could be directly retrieved from multi-view images, depth scans, or LiDAR range images. Different challenges exist in achieving object-centric geometric perception, such as achieving a fine-grained geometric understanding of common articulated objects with multiple rigid parts, learning disentangled shape and pose representations with fewer labels, or tackling dynamic and sequential geometric input in an end-to-end fashion. Here we identify and solve these challenges from a 3D deep learning perspective by designing effective and generalizable 3D representations, architectures, and pipelines. We propose the first deep pose estimation for common articulated objects by designing a novel hierarchical invariant representation. To push the boundary of 6D pose estimation for common rigid objects, a simple yet effective self-supervised framework is designed to handle unlabeled partial segmented scans. We further contribute a novel 4D convolutional neural network called PointMotionNet to learn spatio-temporal features for 3D point cloud sequences. All these works advance the domain of object-centric geometric perception from a unique 3D deep learning perspective. / Doctor of Philosophy / 3D sensors these days are widely equipped on various mobile devices like a depth camera on iPhone, or laser LiDAR sensors on an autonomous driving vehicle. These 3D sensing techniques could help us get accurate measurements of the 3D world. For the field of machine intel- ligence, we also want to build intelligent system and algorithm to learn useful information and understand the 3D world better. We human beings have the incredible ability to sense and understand this 3D world through our visual or tactile system. For example, humans could infer the geometry structure and arrangement of furniture in a room without seeing the full room, we are able to track an 3D object no matter how its appearance, shape and scale changes, we could also predict the future motion of multiple objects based on sequential observation and complex reasoning. Here my work designs various frameworks to learn such 3D information from geometric data represented by a lot of 3D points, which achieves fine-grained geometric understanding of individual objects, and we can help machine tell the target objects' geometry, states, and dynamics. The work in this dissertation serves as building blocks towards a better understanding of this dynamic world.

Utilization of additional information of the pulse for ALS data classification in ragged terrain Abstract The diploma thesis deals with airborne laser scanning filtering problem in sandstone landscape which is characterized by ragged terrain and in our country also by dense vegetation that makes difficult to transit laser pulse to terrain that can lead to lower accuracy of created DTM. In the first part the basic filtering algorithm that are systematic divided into several groups are described. The emphasis is also put on theoretic problems which we have to deal with during the filtering of laser scanner data acquired in sandstone landscape. The main goal of the thesis is to suggest changes in one of the existing algorithm to additional information of the pulse (mainly amplitude and width of the pulse) be used, and to test this method over the real data. At the end the results of the method and its implementation are critically evaluated. Keywords: airborne laser scanning, point cloud segmentation, point cloud classification, sandstone landscape, DTM

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Syfte: Att ta fram en metod för att hjälpa mindre sågverk att bättre tillvarata mesta möjliga virke från en timmerstock. Metod: En kvantitativ studie där tre iterationer genomförts enligt Design Science. Resultat: För att skapa en effektiv algoritm som ska utföra volymberäkningar i ett punktmoln som består av cirka två miljoner punkter i ett industriellt syfte ligger fokus i att algoritmen är snabb och visar rätt data. Det primära målet för att göra algoritmen snabb är att bearbeta punktmolnet ett minimalt antal gånger. Den algoritm som uppfyller delmålen i denna studie är Algoritm C. Algoritmen är både snabb och har en låg standardavvikelse på mätfelen. Algoritm C har komplexiteten O(n) vid analys av delpunktmoln. Implikationer: Med utgångspunkt från denna studies algoritm skulle det vara möjligt att använda stereokamerateknik för att hjälpa mindre sågverk att bättre tillvarata mesta möjliga virke från en timmerstock. Begränsningar: Studiens algoritm har utgått från att inga punkter har skapats inuti stocken vilket skulle kunna leda till felplacerade punkter. Om en stock skulle vara krokig överensstämmer inte stockens centrum med z-axelns placering. Detta är något som skulle kunna innebära att z-värdet hamnar utanför stocken, i extremfall, vilket algoritmen inte kan hantera. / Purpose: To develop a method that can help smaller sawmills to better utilize the greatest possible amount of wood from a log. Method: A quantitative study where three iterations has been made using Design Science. Findings: To create an effective algorithm that will perform volume calculations in a point cloud consisting of about two million points for an industrial purpose, the focus is on the algorithm being fast and that it shows the correct data. The primary goal of making the algorithm quick is to process the point cloud a minimum number of times. The algorithm that meets the goals in this study is Algorithm C. The algorithm is both fast and has a low standard deviation of the measurement errors. Algorithm C has the complexity O(n) in the analysis of sub-point clouds. Implications: Based on this study’s algorithm, it would be possible to use stereo camera technology to help smaller sawmills to better utilize the most possible amount of wood from a log. Limitations: The study’s algorithm assumes that no points have been created inside the log, which could lead to misplaced points. If a log would be crooked, the center of the log would not match the z-axis position. This is something that could mean that the z-value is outside of the log, in extreme cases, which the algorithm cannot handle.

<p dir="ltr">Accurate 3D point clouds are crucial in various fields, and the advancement of software algorithms has facilitated the reconstruction of 3D models from high-quality images. Notably, both single-camera and multi-camera systems have gained popularity in obtaining these images. While single-camera setups offer simplicity and cost-effectiveness, multi-camera systems provide a broader field of view and improved coverage. However, a crucial gap persists, a lack of direct comparison and comprehensive analysis regarding the quality of point clouds acquired from each system. This thesis aims to bridge this gap by evaluating the point cloud quality obtained from both single-camera and multi-camera systems, considering various factors such as lighting conditions, camera settings, and the stability of multi-camera setup in the 3D reconstruction process. Our research also aims to provide insights into how these factors influence the quality and performance of the reconstructed point clouds. By understanding the strengths and limitations of each system, researchers and professionals can make informed decisions when selecting the most suitable 3D imaging approach for their specific applications. To achieve these objectives, we designed and utilized a custom rig with three vertically stacked cameras, each equipped with a fixed camera lens, and maintained uniform lighting conditions. Additionally, we employed a single-camera system with a zoom lens and non uniform lighting conditions. Through noise analysis, our results revealed several crucial findings. The single-camera system exhibited relatively higher noise levels, likely due to non-uniform lighting and the use of a zoom lens. In contrast, the multi-camera system demonstrated lower noise levels, which can be attributed to well-lit conditions and the use of fixed lenses. However, within the multi-camera system, instances of significant instability led to a substantial increase in noise levels in the reconstructed point cloud compared to more stable conditions. Our noise analysis showed the multi-camera system preformed better compared to the single-camera system in terms of noise quality. However, it is crucial to recognize that noise detection also revealed the influence of factors like lighting conditions, camera calibration and camera stability of multi-camera systems on the reconstruction process.</p>

Photogrammetry has become a great tool when analyzing geological outcrops. The Slaufrudalur plutonis located in southeast Iceland and is composed of multiple granitic intrusions. With the help of 3Dmodeling and photogrammetry one of these intrusions is recreated to see if layers in the mountainsMosfell, Bleikitindur and Skeggtindur can be correlated. This project will help to understand thecorrelation of the layers and which places in the pluton should be further investigated in the future.                                Agisoft metashape was used to create a 3D model from aerial photographs, LIME was used to markthe layers in the model and MOVE was used to recreate the intrusion on the model. The 3D modelingshowed that the layers in the different mountains were at very similar altitudes and had very similarthicknesses. The intrusion model acted very similar in Mosfell and Bleikitindur and quite similar inSkeggtindur as well. An extension of the top of the intrusion showed that there is a possibility that theintrusion layer can be found in the northern part of the pluton. These findings show that there is apossibility that the intrusion could have covered the whole pluton. With more research this could leadto more knowledge about how the Slaufrudalur pluton was created. / Fotogrammetri har blivit ett väldigt bra verktyg för att analysera geologiska strukturer. Slaufrudalurplutonen ligger i Sydöstra Island och består av ett flertal felsiska intrusioner. Med hjälp av 3Dmodelering och fotogrammetri ska en av de felsiska intrusionerna i plutonen återskapas för att se ifalllager i Mosfell, Bleikitindur och Skeggtindur kan korreleras med varandra. Det här arbetet kommerbidra till mer förståelse kring Slaufrudalur plutonen och vilka ställen som skulle vara bra att undersökamer noggrant i framtiden.  Agisoft metashape användes för att skapa en 3D modell utifrån flygplansbilder, LIMEanvändes för att markera ut var lagergränserna fanns och MOVE användes för att återskapaintrusionerna i modellen. Modellerna visade att lagrena i bergen var på liknande altitud ochhade ungefär samma tjocklek. I Mosfell och Blekitindur var den återskapade intrusionenväldigt likformig, i Skeggtindur påminde den ganska mycket om hur den var i de andra bergenmen inte lika mycket. En förlängning av toppen på intrusionen visade att det möjligtvis går atthitta lagret i den norra delen av plutonen.Resultaten från det här arbetet visar att det finns en möjlighet att intrusionen kan ha täckt helaplutonen. Med mer forskning kan det här leda till mer kunskap om hur Slaufrudalur plutonenskapades.

The use of virtual reality in engineering tasks, such as in virtual commissioning, has increased steadily in recent years, where a robot, machine or object of interest can be simulated and visualized. Yet, for a more immerse experience, an environment for the object in question needs to be constructed. However, the process for creatingan accurate environment, for a virtual simulation have remained a costly and a long endeavour. Because of this, many digital simulations are performed, either with no environment at all, or present a very basic and abstract representation of an intended environment.The aim of this thesis is to investigate if technologies such as LiDAR and digital photogrammetry could shorten the environment creation process. Therefore, a demonstrative virtual environment was created and analysed, in which the different technologies was investigated and presented in the form of a comprehensive review of the current state of the technologies with in digital recreation. Lastly, a technique specific evaluation of the time requirement, cost and user difficulty was conducted. As the field of LiDAR and digital photogrammetry is too vast to investigate all forms thereof within one project, this thesis is limited to the investigation of static laser scanners and wide lens camera photogrammetry. A semi industrious locale was chosen for digital replication, which through static laser scans and photographs would generate semi-automated 3D models.The resulting 3D models leave much to be desired, as large holes were present throughout the 3D models, sincecertain surfaces are not suitable for neither replication processes. Transparent and reflective surfaces lead to ripple effects within the 3D models geometry and textures. Moreover, certain surfaces, as blank areas for photogrammetry or black coloration for laser scanners led to missing features and model distortions.Yet despite the abnormalities, the majority of the test environment was successfully re-created. An evaluation of the created environments was performed, which list and illustrate with tables and figures the attributes, strengths and weaknesses of each technique. Moreover, technique specific limitations and a spatial analysis was carried out. With the result, seemingly illustrating that photogrammetry creates more visually accurate 3D models in comparison to the laser scanner, yet the laser scanner produces a more spatially accurate result. As such, a selective combination of the techniques can be suggested.Observations and interviews seem to point towards the full scale application, in which an accurate 3D model is re-created without much effort, to currently not exist. As both photogrammetry and static laser scanning require great effort, skill and time in order to create a seemingly perfect solid model. Yet, utilizing either, or both techniques as a template for 3D object creation could reduce the time to create an environment significantly.Furthermore, methods such as digital 3D sculpting could be used in order to remove imperfections and create what is missing from the digitally constructed 3D models. Thereby achieving an accurate result.