Dissertations / Theses on the topic '3D point cloud modeling'
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Dahlin, Johan. "3D Modeling of Indoor Environments." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93999.
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With the aid of modern sensors it is possible to create models of buildings. These sensorstypically generate 3D point clouds and in order to increase interpretability and usability,these point clouds are often translated into 3D models.In this thesis a way of translating a 3D point cloud into a 3D model is presented. The basicfunctionality is implemented using Matlab. The geometric model consists of floors, wallsand ceilings. In addition, doors and windows are automatically identified and integrated intothe model. The resulting model also has an explicit representation of the topology betweenentities of the model. The topology is represented as a graph, and to do this GraphML isused. The graph is opened in a graph editing program called yEd.The result is a 3D model that can be plotted in Matlab and a graph describing the connectivitybetween entities. The GraphML file is automatically generated in Matlab. An interfacebetween Matlab and yEd allows the user to choose which rooms should be plotted.
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Hammoudi, Karim. "Contributions to the 3D city modeling : 3D polyhedral building model reconstruction from aerial images and 3D facade modeling from terrestrial 3D point cloud and images." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00682442.
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The aim of this work is to develop research on 3D building modeling. In particular, the research in aerial-based 3D building reconstruction is a topic very developed since 1990. However, it is necessary to pursue the research since the actual approaches for 3D massive building reconstruction (although efficient) still encounter problems in generalization, coherency, accuracy. Besides, the recent developments of street acquisition systems such as Mobile Mapping Systems open new perspectives for improvements in building modeling in the sense that the terrestrial data (very dense and accurate) can be exploited with more performance (in comparison to the aerial investigation) to enrich the building models at facade level (e.g., geometry, texturing).Hence, aerial and terrestrial based building modeling approaches are individually proposed. At aerial level, we describe a direct and featureless approach for simple polyhedral building reconstruction from a set of calibrated aerial images. At terrestrial level, several approaches that essentially describe a 3D urban facade modeling pipeline are proposed, namely, the street point cloud segmentation and classification, the geometric modeling of urban facade and the occlusion-free facade texturing
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Bose, Saptak. "An integrated approach encompassing point cloud manipulation and 3D modeling for HBIM establishment: a case of study." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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In the case of Cultural Heritage buildings, the need for an effective, exhaustive, efficient method to replicate its state of being in an interactive, three-dimensional environment is today, of paramount importance, both from an engineering as well as a historical point of view. Modern geomatics entails the usage of Terrestrial Laser Scanners (TLS) and photogrammetric modelling from Structure-from-Motion (SfM) techniques to initiate this modelling operation. To realize its eventual existence, the novel Historic Building Information Modelling (HBIM) technique is implemented. A prototype library of parametric objects, based on historic architectural data, HBIM allows the generation of an all-encompassing, three-dimensional model which possesses an extensive array of information pertaining to the structure at hand. This information, be it geometric, architectural, or even structural, can then be used to realize reinforcement requirements, rehabilitation needs, stage of depreciation, method of initial construction, material makeup, historic alterations, etc. In this paper, the study of the San Michele in Acerboli’s church, located in Santarcangelo di Romagna, Italy, is considered. A HBIM model is prepared and its accuracy analyzed. The final model serves as an information repository for the aforementioned Church, able to geometrically define its finest characteristics.
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Truong, Quoc Hung. "Knowledge-based 3D point clouds processing." Phd thesis, Université de Bourgogne, 2013. http://tel.archives-ouvertes.fr/tel-00977434.
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The modeling of real-world scenes through capturing 3D digital data has proven to be both useful andapplicable in a variety of industrial and surveying applications. Entire scenes are generally capturedby laser scanners and represented by large unorganized point clouds possibly along with additionalphotogrammetric data. A typical challenge in processing such point clouds and data lies in detectingand classifying objects that are present in the scene. In addition to the presence of noise, occlusionsand missing data, such tasks are often hindered by the irregularity of the capturing conditions bothwithin the same dataset and from one data set to another. Given the complexity of the underlyingproblems, recent processing approaches attempt to exploit semantic knowledge for identifying andclassifying objects. In the present thesis, we propose a novel approach that makes use of intelligentknowledge management strategies for processing of 3D point clouds as well as identifying andclassifying objects in digitized scenes. Our approach extends the use of semantic knowledge to allstages of the processing, including the guidance of the individual data-driven processing algorithms.The complete solution consists in a multi-stage iterative concept based on three factors: the modeledknowledge, the package of algorithms, and a classification engine. The goal of the present work isto select and guide algorithms following an adaptive and intelligent strategy for detecting objects inpoint clouds. Experiments with two case studies demonstrate the applicability of our approach. Thestudies were carried out on scans of the waiting area of an airport and along the tracks of a railway.In both cases the goal was to detect and identify objects within a defined area. Results show that ourapproach succeeded in identifying the objects of interest while using various data types
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Yang, Xiucheng. "3D modeling of built heritage : from geometric models to HBIM." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD054/document.
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La maquette numérique de bâtiments historiques (Heritage-BIM) est devenue un enjeu majeur dans la modélisation. Le modèle HBIM ainsi obtenu comprend une description paramétrique et sémantique des éléments constitutifs du patrimoine. La thèse présente des méthodes de constructions HBIM à partir de la documentation historique, de nuages de points, de maillage de surfaces et de géométrie solide. Un concept de mesh-to-HBIM est proposé à l'aide de la programmation visuelle, qui permet de transformer les « familles » paramétriques et les structures géométriques en modèles paramétriques et sémantiques HBIM. La modélisation paramétrique HBIM consiste à créer manuellement des Familles Revit paramétriques et une reconstruction de bâtiment semi-automatisée par l'application de scripts Dynamo. Le processus de modélisation sémantique HBIM transforme directement des géométries segmentées de maillages ou de solides vers l'environnement BIM. Les éléments segmentés et individualisés peuvent être stockés et gérés dans cet environnement avec des compléments d'informations d'association entre élémentsHeritage Building Information Modelling (HBIM) is a major issue in heritage documentation and conservation. The obtained HBIM model provides a parametric and semantic description of the heritage elements. This thesis presents methods for the generation of HBIM models from point clouds (obtained by photogrammetry or laser scanning), surface mesh and solid geometry. A concept of solid/mesh-to-HBIM is proposed using Autodesk Dynamo visual programming, which transfers the parametric “Family” and geometric structures to parametric and semantic HBIM models. The parametric HBIM modelling process involves conventional manual parametric “Family” creation and semi-automated building reconstruction by Dynamo. The semantic HBIM modelling process directly transfers the segmented solid geometry and closed mesh-to-BIM environment. The segmented elements can be stored and managed in the BIM environment with attached attributes information and relationships established among the elements
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De, Pellegrini Martin. "Mobile-based 3D modeling : An indepth evaluation for the application to maintenance and supervision." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298024.
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Indoor environment modeling has become a relevant topic in several applications fields including Augmented, Virtual and Mixed Reality. Furthermore, with the Digital Transformation, many industries have moved toward this technology trying to generate detailed models of an environment allowing the viewers to navigate through it or mapping surfaces to insert virtual elements in a real scene. Therefore, this Thesis project has been conducted with the purpose to review well- established deterministic methods for 3D scene reconstruction and researching the state- of- the- art, such as machine learning- based approaches, and a possible implementation on mobile devices. Initially, we focused on the well- established methods such as Structure from Motion (SfM) that use photogrammetry to estimate camera poses and depth using only RGB images. Lastly, the research has been centered on the most innovative methods that make use of machine learning to predict depth maps and camera poses from a video stream. Most of the methods reviewed are completely unsupervised and are based on a combination of two subnetwork, the disparity network (DispNet) for the depth estimation and pose network (PoseNet) for camera pose estimation. Despite the fact that the results in outdoor application show high quality depth map and and reliable odometry, there are still some limitations for the deployment of this technology in indoor environment. Overall, the results are promising.Modellering av inomhusmiljö har blivit ett relevant ämne inom flera applikationsområden, inklusive Augmented, Virtual och Mixed Reality. Dessutom, med den digitala transformationen, har många branscher gått mot denna teknik som försöker generera detaljerade modeller av en miljö som gör det möjligt för tittarna att navigera genom den eller kartlägga ytor för att infoga virtuella element i en riktig scen. Därför har detta avhandlingsprojekt genomförts med syftet att granska väletablerade deterministiska metoder för 3Dscenrekonstruktion och undersöka det senaste inom teknik, såsom maskininlärningsbaserade metoder och en möjlig implementering på mobil. Inledningsvis fokuserade vi på de väletablerade metoderna som Structure From Motion (SfM) som använder fotogrammetri för att uppskatta kameraställningar och djup med endast RGBbilder. Slutligen har forskningen varit inriktad på de mest innovativa metoderna som använder maskininlärning för att förutsäga djupkartor och kameraposer från en videoström. De flesta av de granskade metoderna är helt utan tillsyn och baseras på en kombination av två undernätverk, skillnadsnätverket (DispNet) för djupuppskattning och posenätverk (PoseNet) för kameraposestimering. Trots att resultaten i utomhusanvändning visar djupkarta av hög kvalitet och tillförlitlig vägmätning, finns det fortfarande vissa begränsningar för användningen av denna teknik i inomhusmiljön, men ändå är resultaten lovande.
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Carlsson, Henrik. "Modeling method to visually reconstruct the historical Vasa ship with the help of a 3D scanned point cloud." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10574.
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A point cloud derived from scanning the actual Vasa ship is used for an accurate visualisation. Both manual and automatic mesh techniques where utilized in the modelling of the Vasa ship to overcome problems of poor resolution in the point cloud and computing power. A combination of manual and automatic techniques resulted in a 3D model optimized for use within animation software. The method presented in this paper utilized a method that allows the user to keep control over topology. The polygon count is kept to a minimum and one can still remain certain that the measurements and realism from the point cloud is maintained.
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Fjärdsjö, Johnny, and Zada Nasir Muhabatt. "Kvalitetssäkrad arbetsprocess vid 3D-modellering av byggnader : Baserat på underlag från ritning och 3D-laserskanning." Thesis, KTH, Byggteknik och design, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148822.
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Tidigare vid ombyggnation, försäljning och förvaltning av byggnader som var uppförda innan 80-talet utgick fastighetsägarna från enkla handritade pappersritningar. Det är en svår utmaning att hålla ritningen uppdaterad till verkliga förhållanden d.v.s. relationsritning. För ca 25 år sedan (i början på 80-talet) byttes papper och penna ut mot avancerade ritprogram (CAD) för framtagning av ritningar. Idag används CAD i stort sett för all nyprojektering och de senaste åren har utvecklingen gått mot en större användning av 3D-underlag än tidigare 2D-ritningar. Den stora fördelen med att projektera i 3D är att en virtuell modell skapas av hela byggnaden för att få en bättre kontroll av ingående byggdelsobjekt och även att fel kan upptäckas i tidigare skeden än på byggarbetsplatsen. Genom att börja bygga en virtuell byggnad i 3D från första skedet och succesivt fylla den med mer relevant information i hela livscykeln får man en komplett informationsmodell. Ett av kraven som ställs på fastighetsägarna vid ombyggnation och förvaltning är att tillhandahålla korrekt information och uppdaterade ritningar. Det skall vara enkelt för entreprenören att avläsa ritningarna. I rapporten beskrivs en effektiviserad arbetsprocess, metoder, verktyg och användningsområden för framtagning av 3D-modeller. Detta arbete avser att leda fram till en metodbeskrivning som skall användas för erfarenhetsåterföring. Arbetet skall också vara ett underlag som skall användas för att beskriva tillvägagångsättet att modellera från äldre ritningar till 3D-modeller. Metodbeskrivningen kommer att förenkla förståelsen för modellering för både fastighetsägaren och inom företaget, samt höja kvalitén på arbetet med att skapa CAD-modeller från de olika underlag som används för modellering.The use of hand drawn construction model was the only way of development, rebuilding, sales and real estate management before the 80’s. However, the challenge was to preserve the drawings and maintain its real condition. To make things work faster and easier the development of advanced drawing software (CAD) was introduced which replaced the traditional hand drawn designs. Today, CAD is used broadly for all new constructions with a great success rate. However, with the new advanced technology many engineers and construction companies are heavily using 3D models over 2D drawings. The major advantage of designing in 3D is a virtual model created of the entire building to get a better control of input construction items and the errors can be detected at earlier stages than at the construction sites. By modifying buildings in a virtual model in three dimensions yet at the first stage and gradually fill it with more relevant information throughout the life cycle of buildings to get a complete information model. One of the requirements from the property owners in the redevelopment and management is to provide accurate information and updated drawings. It should be simple for the contractor to read drawings. This report describes a streamlined work processes, methods, tools and applications for the production of 3D models. This work is intended to lead to a methodology and to be used as well as for passing on experience. This report will also be a base to describe the approach to model from older drawings into 3D models. The method description will simplify the understanding of model for both the property owners and for companies who creates 3D models. It will also increase the quality of the work to create CAD models from the different data used for modeling.
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Abayowa, Bernard Olushola. "Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of Large Scale City Models." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1372508452.
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Penk, David. "Vyhotovení 3D modelu části budovy SPŠ stavební Brno." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2021. http://www.nusl.cz/ntk/nusl-444256.
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The thesis deals with the creation of a 3D model from data collected by laser scanning. The first part deals with the theoretical foundations of buildings information modeling and method of laser scanning. The rest of the work describes the detailed process from data collection to the creation of the model. Most of the space is devoted to work in the Revit software environment.
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Nyman, Jonas. "Faster Environment Modelling and Integration into Virtual Reality Simulations." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19800.
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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.
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Murtiyoso, Arnadi Dhestaratri. "Relevé 3D et classification de nuages de points du patrimoine bâti." Thesis, Strasbourg, 2020. http://www.theses.fr/2020STRAD007.
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La documentation du patrimoine bâti a beaucoup évolué ces dernières années grâce au développement de nouveaux capteurs 3D et de nouvelles techniques de relevé 3D. Les données 3D contribuent à la création d'archives fiables et tangibles des sites et des monuments historiques. Vu l'importance des données 3D dans la documentation du patrimoine bâti, le contrôle de qualité est un aspect primordial qui devrait être abordé avant d'entreprendre le traitement du nuage de points. La thèse est ainsi divisée en deux parties. La première partie concerne principalement l'acquisition et le contrôle de qualité des données. Un point important sera l'intégration de la photogrammétrie et de lalaser grammétrie dans le contexte de la documentation d'un site historique à différentes échelles. La deuxième partie de la thèse va aborder le traitement de nuages de points, plus particulièrement la segmentation et la classification de nuages de points. L'aspect multi-échelle de notre approche est importante car dans beaucoup de cas, un bâtiment remarquable se situe dans un quartier historique qui nécessite une segmentation multi-échelle. En combinant ces deux parties, nous avons considéré l'ensemble du processus allant de l'acquisition de données 3D jusqu'à la segmentation et la classification en entités à plusieurs échellesThe documentation of built heritage has seen a significant development these past few decades due to advancements in new 3D sensors and 3D recording techniques. 3D data serve as reliable and tangible archive for historical sites and monuments. Since 3D data have such importance in the field of heritage documentation, quality control is paramount and must be performed before any point cloud processing is even planned to be conducted. The thesis is therefore divided into two parts. The first part concerned mainly the data acquisition and quality control of the point cloud data using the two techniques most commonly used, i.e. photogrammetry and laser scanning. A particular emphasis was also put on the integration of photogrammetry and laser scanning within the context of a multi-scalar documentation of a heritage site. The second part will address the processing of the resulting point cloud, particularly its segmentation and classification. The multi-scalar approach proposed in this thesis is an important point to note, as in many cases a historical building of interest is located in a historical neighbourhood; thus the requirement for a multi-scalar segmentation. By combining these two parts, the thesis had attempted to address the 3D workflow of heritage sites in a holistic manner, from the 3D data acquisition up to the resulting point clouds' segmentation and classification into individual entities in various scale steps
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Estima, Maria Inês Duarte Ramos. "Comparação de modelos tridimensionais produzidos com imagens adquiridas por UAV e avaliação de volumes." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/16919.
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Mestrado em GeoinformáticaNa área da geoengenharia, um dos grandes obstáculos sempre foram os custos associados às operações inerentes à realização de trabalhos nos diferentes tipos de serviços e produtos que se podem elaborar nesta tão vasta área. É importante salientar como grande benefício a utilização de novas ou mais recentes tecnologias que minimizem estes custos, e tenham como resultados iguais ou melhores produtos e serviços, comparativamente com os realizados recorrendo a métodos e tecnologias tradicionais. Contudo, e como resultado destas necessidades, foram surgindo nos últimos tempos inúmeros sistemas, de inúmeros fornecedores, nas diversas variantes da geoengenharia, facto que se traduz numa larga lista de possibilidades de utilização de equipamentos e ferramentas para estes fins. Assim, nesta dissertação, para além de avaliar a exatidão e precisão destas novas tecnologias comparativamente com métodos tradicionais, pretende-se também estabelecer comparação dentro das novas tecnologias, entre as várias possibilidades existentes no mercado. Os temas de dissertação apresentados enquadram-se no projeto “ROADMAP BCP BAIÃO”, que consiste na Modelação 3D, com base em dados levantados com recurso a UAV, e posterior comparação e avaliação de resultados dos diferentes modelos gerados com recurso a vários softwares (Parte 1 da dissertação), e “FERROVIAL Aterro 16”, para cálculo de volumes, com base em dados de aterro sanitário levantados com recurso a UAV para posterior comparação com cálculo efetuado com base em topografia, e dados de maquetes com volumes conhecidos, para comparação (Parte 2 da dissertação) , a desenvolver pela Geolayer – Estudos de Território, Lda. Na primeira parte serão abordados os trabalhos inerentes ao desenvolvimento e criação de ambiente tridimensional com a finalidade de promoção de propriedade no mercado imobiliário, permitindo a manipulação e visualização 3D por parte da entidade detentora da mesma, mas principalmente por possíveis compradores. A propriedade situa-se no concelho de Baião, é propriedade da instituição bancária Millenuium BCP, e é composta por terreno e edificação que se encontra em zona de acesso condicionado. A segunda parte da dissertação assentará num teste de comparação entre volumes de aterro calculados com dados levantados com topografia, e volumes de aterro calculados com dados levantados com recurso a sistema UAV. O aterro sanitário em causa designa-se por “Aterro do Planalto Beirão”, situa-se no concelho de Tondela, e é concessionado pela entidade agora responsável FERROVIAL SERVIÇOS SA.
In the area of geoengineering, one of the major obstacles have always been the costs associated with the operations involved in carrying out work in different types of services and products that may develop in this vast area. It is important to point out how great is the use of new or newer technologies that minimize these costs, and have as results equal to or better products and services, compared to those made using traditional methods and technologies. However, as a result of these needs have appeared in recent times numerous systems, from many providers vendors of geoengineering systems fact which translates into a long list of possibilities for using equipment and tools for these purposes. Thus, in this thesis, in addition to evaluating the accuracy and precision of these new technologies compared to traditional methods, it is intended to also establish comparison within the new technologies among the various possibilities on the market. The presented dissertation topics fall within the project "ROADMAP BCP BAIÃO", which consists of 3D modeling based on data collected using the UAV, and subsequent comparison and evaluation of results of different models generated using various software (Part 1 of the dissertation), and "FERROVIAL ATERRO 16" for volumes calculation, based on landfill of data collected using the UAV to be compared with calculations made based on topography, and models of data with known volumes for comparison (Part 2 of the dissertation), to be developed by Geolayer - Estudos de Território, Lda. The first part will deal with the inherent development work and creating three-dimensional environment for property promotion purpose in the housing market, allowing the manipulation and 3D visualization by the entity holding it, but mainly for potential buyers. The property is located in the municipality of Baião, and is owned by the bank Millenuium BCP, and is composed of land and building which is in restricted access zone. The second part of the dissertation will be based on a comparison test between landfill volumes calculated with data collected with topography, and landfill volumes calculated with data collected using UAV system. The landfill in question is called a “Aterro do Planalto Beirão”, is located in Tondela municipality, and is now responsible for the concession entity FERROVIAL SERVIÇOS SA.
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Smith, Michael. "Non-parametric workspace modelling for mobile robots using push broom lasers." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:50224eb9-73e8-4c8a-b8c5-18360d11e21b.
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This thesis is about the intelligent compression of large 3D point cloud datasets. The non-parametric method that we describe simultaneously generates a continuous representation of the workspace surfaces from discrete laser samples and decimates the dataset, retaining only locally salient samples. Our framework attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity. The work presented here has a specific focus on gathering and processing laser measurements taken from a moving platform in outdoor workspaces. We introduce a somewhat unusual parameterisation of the problem and look to Gaussian Processes as the fundamental machinery in our processing pipeline. Our system compresses laser data in a fashion that is naturally sympathetic to the underlying structure and complexity of the workspace. In geometrically complex areas, compression is lower than that in geometrically bland areas. We focus on this property in detail and it leads us well beyond a simple application of non-parametric techniques. Indeed, towards the end of the thesis we develop a non-stationary GP framework whereby our regression model adapts to the local workspace complexity. Throughout we construct our algorithms so that they may be efficiently implemented. In addition, we present a detailed analysis of the proposed system and investigate model parameters, metric errors and data compression rates. Finally, we note that this work is predicated on a substantial amount of robotics engineering which has allowed us to produce a high quality, peer reviewed, dataset - the first of its kind.
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Roure, Garcia Ferran. "Tools for 3D point cloud registration." Doctoral thesis, Universitat de Girona, 2017. http://hdl.handle.net/10803/403345.
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In this thesis, we did an in-depth review of the state of the art of 3D registration, evaluating the most popular methods. Given the lack of standardization in the literature, we also proposed a nomenclature and a classification to unify the evaluation systems and to be able to compare the different algorithms under the same criteria.
The major contribution of the thesis is the Registration Toolbox, which consists of software and a database of 3D models. The software presented here consists of a 3D Registration Pipeline written in C ++ that allows researchers to try different methods, as well as add new ones and compare them. In this Pipeline, we not only implemented the most popular methods of literature, but we also added three new methods that contribute to improving the state of the art. On the other hand, the database provides different 3D models to be able to carry out the tests to validate the performances of the methods. Finally, we presented a new hybrid data structure specially focused on the search for neighbors. We tested our proposal together with other data structures and we obtained very satisfactory results, overcoming in many cases the best current alternatives. All tested structures are also available in our Pipeline.
This Toolbox is intended to be a useful tool for the whole community and is available to researchers under a Creative Commons licenseEn aquesta tesi, hem fet una revisió en profunditat de l'estat de l'art del registre 3D, avaluant els mètodes més populars. Donada la falta d'estandardització de la literatura, també hem proposat una nomenclatura i una classificació per tal d'unificar els sistemes d'avaluació i poder comparar els diferents algorismes sota els mateixos criteris. La contribució més gran de la tesi és el Toolbox de Registre, que consisteix en un software i una base de dades de models 3D. El software presentat aquí consisteix en una Pipeline de registre 3D escrit en C++ que permet als investigadors provar diferents mètodes, així com afegir-n'hi de nous i comparar-los. En aquesta Pipeline, no només hem implementat els mètodes més populars de la literatura, sinó que també hem afegit tres mètodes nous que contribueixen a millorar l'estat de l'art de la tecnologia. D'altra banda, la base de dades proporciona una sèrie de models 3D per poder dur a terme les proves necessàries per validar el bon funcionament dels mètodes. Finalment, també hem presentat una nova estructura de dades híbrida especialment enfocada a la cerca de veïns. Hem testejat la nostra proposta conjuntament amb altres estructures de dades i hem obtingut resultats molt satisfactoris, superant en molts casos les millors alternatives actuals. Totes les estructures testejades estan també disponibles al nostre Pipeline. Aquesta Toolbox està pensada per ésser una eina útil per tota la comunitat i està a disposició dels investigadors sota llicència Creative-Commons
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Tarcin, Serkan. "Fast Feature Extraction From 3d Point Cloud." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615659/index.pdf.
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To teleoperate an unmanned vehicle a rich set of information should be gathered from surroundings.These systems use sensors which sends high amounts of data and processing the data in CPUs can be time consuming. Similarly, the algorithms that use the data may work slow because of the amount of the data. The solution is, preprocessing the data taken from the sensors on the vehicle and transmitting only the necessary parts or the results of the preprocessing. In this thesis a 180 degree laser scanner at the front end of an unmanned ground vehicle (UGV) tilted up and down on a horizontal axis and point clouds constructed from the surroundings. Instead of transmitting this data directly to the path planning or obstacle avoidance algorithms, a preprocessing stage has been run. In this preprocess rst, the points belonging to the ground plane have been detected and a simplied version of ground has been constructed then the obstacles have been detected. At last, a simplied ground plane as ground and simple primitive geometric shapes as obstacles have been sent to the path planning algorithms instead of sending the whole point cloud.
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Forsman, Mona. "Point cloud densification." Thesis, Umeå universitet, Institutionen för fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39980.
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Several automatic methods exist for creating 3D point clouds extracted from 2D photos. In manycases, the result is a sparse point cloud, unevenly distributed over the scene.After determining the coordinates of the same point in two images of an object, the 3D positionof that point can be calculated using knowledge of camera data and relative orientation. A model created from a unevenly distributed point clouds may loss detail and precision in thesparse areas. The aim of this thesis is to study methods for densification of point clouds. This thesis contains a literature study over different methods for extracting matched point pairs,and an implementation of Least Square Template Matching (LSTM) with a set of improvementtechniques. The implementation is evaluated on a set of different scenes of various difficulty. LSTM is implemented by working on a dense grid of points in an image and Wallis filtering isused to enhance contrast. The matched point correspondences are evaluated with parameters fromthe optimization in order to keep good matches and discard bad ones. The purpose is to find detailsclose to a plane in the images, or on plane-like surfaces. A set of extensions to LSTM is implemented in the aim of improving the quality of the matchedpoints. The seed points are improved by Transformed Normalized Cross Correlation (TNCC) andMultiple Seed Points (MSP) for the same template, and then tested to see if they converge to thesame result. Wallis filtering is used to increase the contrast in the image. The quality of the extractedpoints are evaluated with respect to correlation with other optimization parameters and comparisonof standard deviation in x- and y- direction. If a point is rejected, the option to try again with a largertemplate size exists, called Adaptive Template Size (ATS).
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Gujar, Sanket. "Pointwise and Instance Segmentation for 3D Point Cloud." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1290.
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The camera is the cheapest and computationally real-time option for detecting or segmenting the environment for an autonomous vehicle, but it does not provide the depth information and is undoubtedly not reliable during the night, bad weather, and tunnel flash outs. The risk of an accident gets higher for autonomous cars when driven by a camera in such situations. The industry has been relying on LiDAR for the past decade to solve this problem and focus on depth information of the environment, but LiDAR also has its shortcoming. The industry methods commonly use projections methods to create a projection image and run detection and localization network for inference, but LiDAR sees obscurants in bad weather and is sensitive enough to detect snow, making it difficult for robustness in projection based methods. We propose a novel pointwise and Instance segmentation deep learning architecture for the point clouds focused on self-driving application. The model is only dependent on LiDAR data making it light invariant and overcoming the shortcoming of the camera in the perception stack. The pipeline takes advantage of both global and local/edge features of points in points clouds to generate high-level feature. We also propose Pointer-Capsnet which is an extension of CapsNet for small 3D point clouds.
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Chen, Chen. "Semantics Augmented Point Cloud Sampling for 3D Object Detection." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26956.
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3D object detection is an emerging topic among both industries and research communities. It aims at discovering objects of interest from 3D scenes and has a strong connection with many real-world scenarios, such as autonomous driving.
Currently, many models have been proposed to detect potential objects from point clouds. Some methods attempt to model point clouds in the unit of point, and then perform detection with acquired point-wise features. These methods are classified as point-based methods. However, we argue that the prevalent sampling algorithm for point-based models is sub-optimal for involving too much potentially unimportant data and may also lose some important information for detecting objects. Hence, it may lead to a significant performance drop.
This thesis manages to improve the current sampling strategy for point-based models in the context of 3D detection. We propose recasting the sampling algorithm by incorporating semantic information to help identify more beneficial data for detection, thus obtaining a semantics augmented sampling strategy. In particular, we introduce a 2-phase augmentation for sampling. In the point feature learning phase, we propose a semantics-guided farthest point sampling (S-FPS) to keep more informative foreground points. In addition, in the box prediction phase, we devise a semantic balance sampling (SBS) to avoid redundant training on easily recognized instances.
We evaluate our proposed strategy on the popular KITTI dataset and the large-scale nuScenes dataset. Extensive experiments show that our method lifts the point-based single-stage detector to surpass all existing point-based models and even achieve comparable performance to state-of-the-art two-stage methods.
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Dey, Emon Kumar. "Effective 3D Building Extraction from Aerial Point Cloud Data." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/413311.
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Building extraction is important for a wider range of applications including smart city planning, disaster management, security, and cadastral mapping. This thesis mainly aims to present an effective data-driven strategy for building extraction using aerial Light Detection And Ranging (LiDAR) point cloud data. The LiDAR data provides highly accurate three-dimensional (3D) positional information. Therefore, studies on building extraction using LiDAR data have broadened in scope over time. Outliers, inharmonious input data behaviour, innumerable building structure possibilities, and heterogeneous environments are major challenges that need to be addressed for an effective 3D building extraction using LiDAR data. Outliers can cause the extraction of erroneous roof planes, incorrect boundaries, and over-segmentation of the extracted buildings. Due to the uneven point densities and heterogeneous building structures, small roof parts often remain undetected. Moreover, finding and using a realistic performance metric to evaluate the extracted buildings is another challenge. Inaccurate identification of sharp features, coplanar points, and boundary feature points often causes inaccurate roof plane segmentation and overall 3D outline generation for a building. To address these challenges, first, this thesis proposes a robust variable point neighbourhood estimation method. Considering the specific scanline properties associated with aerial LiDAR data, the proposed method automatically estimates an optimal and realistic neighbourhood for each point to solve the shortcomings of existing fixed neighbourhood methods in uneven or abrupt point densities. Using the estimated variable neighbourhood, a robust z-score and a distance-based outlier factor are calculated for each point in the input data. Based on these two measurements, an effective outlier detection method is proposed which can preserve more than 98% of inliers and remove outliers with better precision than the existing state-of-the-art methods. Then, individual roof planes are extracted in a robust way from the separated outlier free coplanar points based on the M-estimator SAmple Consensus (MSAC) plane-ftting algorithm. The proposed technique is capable of extracting small real roof planes, while avoiding spurious roof planes caused by the remaining outliers, if any. Individual buildings are then extracted precisely by grouping adjacent roof planes into clusters. Next, to assess the extracted buildings and individual roof plane boundaries, a realistic evaluation metric is proposed based on a new robust corner correspondence algorithm. The metric is defined as the average minimum distance davg from the extracted boundary points to their actual corresponding reference lines. It strictly follows the definition of a standard mathematical metric, and addresses the shortcomings of the existing metrics. In addition, during the evaluation, the proposed metric separately identifies the underlap and extralap areas in an extracted building. Furthermore, finding precise 3D feature points (e.g., fold and boundary) is necessary for tracing feature lines to describe a building outline. It is also important for accurate roof plane extraction and for establishing relationships between the correctly extracted planes so as to facilitate a more robust 3D building extraction. Thus, this thesis presents a robust fold feature point extraction method based on the calculated normal of the individual point. Later, a method to extract the feature points representing the boundaries is also developed based on the distance from a point to the calculated mean of its estimated neighbours. In the context of the accuracy evaluation, the proposed methods show more than 90% F1-scores on the generated ground truth data. Finally, machine learning techniques are applied to circumvent the problems (e.g., selecting manual thresholds for different parameters) of existing rule-based approaches for roof feature point extraction and classification. Seven effective geometric and statistical features are calculated for each point to train and test the machine learning classifiers using the appropriate ground truth data. Four primary classes of building roof point cloud are considered, and promising results for each of the classes have been achieved, confirming the competitive performance of the classification over the state-of-the-art techniques. At the end of this thesis, using the classified roof feature points, a more robust plane segmentation algorithm is demonstrated for extracting the roof planes of individual buildings.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Eckart, Benjamin. "Compact Generative Models of Point Cloud Data for 3D Perception." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1089.
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One of the most fundamental tasks for any robotics application is the ability to adequately assimilate and respond to incoming sensor data. In the case of 3D range sensing, modern-day sensors generate massive quantities of point cloud data that strain available computational resources. Dealing with large quantities of unevenly sampled 3D point data is a great challenge for many fields, including autonomous driving, 3D manipulation, augmented reality, and medical imaging. This thesis explores how carefully designed statistical models for point cloud data can facilitate, accelerate, and unify many common tasks in the area of range-based 3D perception. We first establish a novel family of compact generative models for 3D point cloud data, offering them as an efficient and robust statistical alternative to traditional point-based or voxel-based data structures. We then show how these statistical models can be utilized toward the creation of a unified data processing architecture for tasks such as segmentation, registration, visualization, and mapping. In complex robotics systems, it is common for various concurrent perceptual processes to have separate low-level data processing pipelines. Besides introducing redundancy, these processes may perform their own data processing in conflicting or ad hoc ways. To avoid this, tractable data structures and models need to be established that share common perceptual processing elements. Additionally, given that many robotics applications involving point cloud processing are size, weight, and power-constrained, these models and their associated algorithms should be deployable in low-power embedded systems while retaining acceptable performance. Given a properly flexible and robust point processor, therefore, many low-level tasks could be unified under a common architectural paradigm and greatly simplify the overall perceptual system. In this thesis, a family of compact generative models is introduced for point cloud data based on hierarchical Gaussian Mixture Models. Using recursive, dataparallel variants of the Expectation Maximization algorithm, we construct high fidelity statistical and hierarchical point cloud models that compactly represent the data as a 3D generative probability distribution. In contrast to raw points or voxelbased decompositions, our proposed statistical model provides a better theoretical footing for robustly dealing with noise, constructing maximum likelihood methods, reasoning probabilistically about free space, utilizing spatial sampling techniques, and performing gradient-based optimizations. Further, the construction of the model as a spatial hierarchy allows for Octree-like logarithmic time access. One challenge compared to previous methods, however, is that our model-based approach incurs a potentially high creation cost. To mitigate this problem, we leverage data parallelism in order to design models well-suited for GPU acceleration, allowing them to run at real-time rates for many time-critical applications. We show how our models can facilitate various 3D perception tasks, demonstrating state-of-the-art performance in geometric segmentation, registration, dynamic occupancy map creation, and 3D visualization.
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Oropallo, William Edward Jr. "A Point Cloud Approach to Object Slicing for 3D Printing." Thesis, University of South Florida, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751757.
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Various industries have embraced 3D printing for manufacturing on-demand, custom printed parts. However, 3D printing requires intelligent data processing and algorithms to go from CAD model to machine instructions. One of the most crucial steps in the process is the slicing of the object. Most 3D printers build parts by accumulating material layers by layer. 3D printing software needs to calculate these layers for manufacturing by slicing a model and calculating the intersections. Finding exact solutions of intersections on the original model is mathematically complicated and computationally demanding. A preprocessing stage of tessellation has become the standard practice for slicing models. Calculating intersections with tessellations of the original model is computationally simple but can introduce inaccuracies and errors that can ruin the final print.
This dissertation shows that a point cloud approach to preprocessing and slicing models is robust and accurate. The point cloud approach to object slicing avoids the complexities of directly slicing models while evading the error-prone tessellation stage. An algorithm developed for this dissertation generates point clouds and slices models within a tolerance. The algorithm uses the original NURBS model and converts the model into a point cloud, based on layer thickness and accuracy requirements. The algorithm then uses a gridding structure to calculate where intersections happen and fit B-spline curves to those intersections.
This algorithm finds accurate intersections and can ignore certain anomalies and error from the modeling process. The primary point evaluation is stable and computationally inexpensive. This algorithm provides an alternative to challenges of both the direct and tessellated slicing methods that have been the focus of the 3D printing industry.
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Lev, Hoang Justin. "A Study of 3D Point Cloud Features for Shape Retrieval." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM040.
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Grâce à l’amélioration et la multiplication des capteurs 3D, la diminution des prix et l’augmentation des puissances de calculs, l’utilisation de donnée3D s’est intensifiée ces dernières années. Les nuages de points 3D (3D pointcloud) sont une des représentations possibles pour de telles données. Elleà l’avantage d’être simple et précise, ainsi que le résultat immédiat de la capture. En tant que structure non-régulière sous forme de liste de points,l’analyse des nuages de points est complexe d’où leur récente utilisation. Cette thèse se concentre sur l’utilisation de nuages de points 3D pourune analyse tridimensionnelle de leur forme. La géométrie des nuages est plus particulièrement étudiée via les courbures des objets. Des descripteursreprésentant la distribution des courbures principales sont proposés: Semantic Point Cloud (SPC) et Multi-Scale Principal Curvature Point Cloud (MPC2).Global Local Point Cloud (GLPC) est un autre descripteur basé sur les courbures mais en combinaison d’autres propriétés. Ces trois descripteurs sontrobustes aux erreurs communes lors d’une capture 3D comme par exemple le bruit ou bien les occlusions. Leurs performances sont supérieures à ceuxde l’état de l’art en ce qui concerne la reconnaissance d’instance avec plus de 90% de précision. La thèse étudie également les récents algorithmes de deep learning qui concernent les nuages de points 3D qui sont apparus au cours de ces trois ans de thèse. Une première approche utilise des descripteurs basé sur les courbures en tant que données d’entrée pour un réseau de perceptron multicouche (MLP). Les résultats ne sont cependant pas au niveau de l’état de l’art mais cette étude montre que ModelNet, la base de données de référence pour laclassification d’objet 3D, n’est pas optimale. En effet, la base de donnéesn’est pas une bonne représentation de la réalité en ne reflétant pas la richesse de courbures des objets réels. Enfin, l’architecture d’un réseau neuronal artificiel est présenté. Inspiré par l’état de l’art en deep learning, Multi-scale PointNet détermine les propriétés d’un objet à différente échelle et les combine afin de le décrire. Encore en développement, le modèle requiert encore des ajustements pour obtenir des résultats concluants. Pour résumer, en s’attaquant au problème complexe de l’utilisation des nuages de points 3D mais aussi à l’évolution rapide du domaine, la thèse contribue à l’état de l’art sur trois aspects majeurs: (i) L’élaboration de nouveaux algorithmes se basant sur les courbures géométrique des objets pour la reconnaissance d’instance. (ii) L’étude qui montre que la construction d’une nouvelle base de données plus réaliste est nécessaire pour correctement poursuivre les études dans le domaine. (iii) La proposition d’une nouvelle architecture de réseau de neurones artificiels pour l’analyse de nuage de points3DWith the improvement and proliferation of 3D sensors, price cut and enhancementof computational power, the usage of 3D data intensifies for the last few years. The3D point cloud is one type amongst the others for 3D representation. This particularlyrepresentation is the direct output of sensors, accurate and simple. As a non-regularstructure of unordered list of points, the analysis on point cloud is challenging andhence the recent usage only.This PhD thesis focuses on the use of 3D point cloud representation for threedimensional shape analysis. More particularly, the geometrical shape is studied throughthe curvature of the object. Descriptors describing the distribution of the principalcurvature is proposed: Principal Curvature Point Cloud and Multi-Scale PrincipalCurvature Point Cloud. Global Local Point Cloud is another descriptor using thecurvature but in combination with other features. These three descriptors are robustto typical 3D scan error like noisy data or occlusion. They outperform state-of-the-artalgorithms in instance retrieval task with more than 90% of accuracy.The thesis also studies deep learning on 3D point cloud which emerges during thethree years of this PhD. The first approach tested, used curvature-based descriptor asthe input of a multi-layer perceptron network. The accuracy cannot catch state-ofthe-art performances. However, they show that ModelNet, the standard dataset for 3Dshape classification is not a good picture of the reality. Indeed, the experiment showsthat the dataset does not reflect the curvature wealth of true objects scans.Ultimately, a new neural network architecture is proposed. Inspired by the state-ofthe-art deep learning network, Multiscale PointNet computes the feature on multiplescales and combines them all to describe an object. Still under development, theperformances are still to be improved.In summary, tackling the challenging use of 3D point clouds but also the quickevolution of the field, the thesis contributes to the state-of-the-art in three majoraspects: (i) Design of new algorithms, relying on geometrical curvature of the objectfor instance retrieval task. (ii) Study and exhibition of the need to build a new standardclassification dataset with more realistic objects. (iii) Proposition of a new deep neuralnetwork for 3D point cloud analysis
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Kulkarni, Amey S. "Motion Segmentation for Autonomous Robots Using 3D Point Cloud Data." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1370.
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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.
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He, Linbo. "Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data : Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157705.
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Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. On the first two problems, CNNs have achieved remarkable progress, but on point cloud segmentation, the results are less satisfactory due to challenges such as limited memory resource and difficulties in 3D point annotation. One of the research studies carried out by the Computer Vision Lab at Linköping University was aiming to ease the semantic segmentation of 3D point cloud. The idea is that by first projecting 3D data points to 2D space and then focusing only on the analysis of 2D images, we can reduce the overall workload for the segmentation process as well as exploit the existing well-developed 2D semantic segmentation techniques. In order to improve the performance of CNNs for 2D semantic segmentation, the study has used input data derived from different modalities. However, how different modalities can be optimally fused is still an open question. Based on the above-mentioned study, this thesis aims to improve the multistream framework architecture. More concretely, we investigate how different singlestream architectures impact the multistream framework with a given fusion method, and how different fusion methods contribute to the overall performance of a given multistream framework. As a result, our proposed fusion architecture outperformed all the investigated traditional fusion methods. Along with the best singlestream candidate and few additional training techniques, our final proposed multistream framework obtained a relative gain of 7.3\% mIoU compared to the baseline on the semantic3D point cloud test set, increasing the ranking from 12th to 5th position on the benchmark leaderboard.
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Downham, Alexander David. "True 3D Digital Holographic Tomography for Virtual Reality Applications." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513204001924421.
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Trowbridge, Michael Aaron. "Autonomous 3D Model Generation of Orbital Debris using Point Cloud Sensors." Thesis, University of Colorado at Boulder, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1558774.
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A software prototype for autonomous 3D scanning of uncooperatively rotating orbital debris using a point cloud sensor is designed and tested. The software successfully generated 3D models under conditions that simulate some on-orbit orbit challenges including relative motion between observer and target, inconsistent target visibility and a target with more than one plane of symmetry. The model scanning software performed well against an irregular object with one plane of symmetry but was weak against objects with 2 planes of symmetry.
The suitability of point cloud sensors and algorithms for space is examined. Terrestrial Graph SLAM is adapted for an uncooperatively rotating orbital debris scanning scenario. A joint EKF attitude estimate and shape similiarity loop closure heuristic for orbital debris is derived and experimentally tested. The binary Extended Fast Point Feature Histogram (EFPFH) is defined and analyzed as a binary quantization of the floating point EFPFH. Both the binary and floating point EPFH are experimentally tested and compared as part of the joint loop closure heuristic.
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Diskin, Yakov. "Dense 3D Point Cloud Representation of a Scene Using Uncalibrated Monocular Vision." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366386933.
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Hirschmüller, Korbinian. "Development and Evaluation of a 3D Point Cloud Based Attitude Determination System." Thesis, Luleå tekniska universitet, Rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65910.
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Blahož, Vladimír. "Vizualizace 3D scény pro ovládání robota." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236501.
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This thesis presents possibilities of 3D point cloud and true colored digital video fusion that can be used in the process of robot teleoperation. Advantages of a 3D environment visualization combining more than one sensor data, tools to facilitate such data fusion, as well as two alternative practical implementations of combined data visualization are discussed. First proposed alternative estimates view frustum of the robot's camera and maps real colored video to a semi-transparent polygon placed in the view frustum. The second option is a direct coloring of the point cloud data creating a colored point cloud representing color as well as depth information about an environment.
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Aronsson, Oskar, and Julia Nyman. "Boundary Representation Modeling from Point Clouds." Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278543.
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Inspections of bridges are today performed ocularly by an inspector at arm’s lengths distance to evaluate damages and to assess its current condition. Ocular inspections often require specialized equipment to aid the inspector to reach all parts of the bridge. The current state of practice for bridge inspection is therefore considered to be time-consuming, costly, and a safety hazard for the inspector. The purpose of this thesis has been to develop a method for automated modeling of bridges from point cloud data. Point clouds that have been created through photogrammetry from a collection of images acquired with an Unmanned Aerial Vehicle (UAV). This thesis has been an attempt to contribute to the long-term goal of making bridge inspections more efficient by using UAV technology. Several methods for the identification of structural components in point clouds have been evaluated. Based on this, a method has been developed to identify planar surfaces using the model-fitting method Random Sample Consensus (RANSAC). The developed method consists of a set of algorithms written in the programming language Python. The method utilizes intersection points between planes as well as the k-Nearest-Neighbor (k-NN) concept to identify the vertices of the structural elements. The method has been tested both for simulated point cloud data as well as for real bridges, where the images were acquired with a UAV. The results from the simulated point clouds showed that the vertices were modeled with a mean deviation of 0.13− 0.34 mm compared to the true vertex coordinates. For a point cloud of a rectangular column, the algorithms identified all relevant surfaces and were able to reconstruct it with a deviation of less than 2 % for the width and length. The method was also tested on two point clouds of real bridges. The algorithms were able to identify many of the relevant surfaces, but the complexity of the geometries resulted in inadequately reconstructed models.Besiktning av broar utförs i dagsläget okulärt av en inspektör som på en armlängds avstånd bedömer skadetillståndet. Okulär besiktning kräver därmed ofta speciell utrustning för att inspektören ska kunna nå samtliga delar av bron. Detta resulterar i att det nuvarande tillvägagångssättet för brobesiktning beaktas som tidkrävande, kostsamt samt riskfyllt för inspektören. Syftet med denna uppsats var att utveckla en metod för att modellera broar på ett automatiserat sätt utifrån punktmolnsdata. Punktmolnen skapades genom fotogrammetri, utifrån en samling bilder tagna med en drönare. Uppsatsen har varit en insats för att bidra till det långsiktiga målet att effektivisera brobesiktning genom drönarteknik. Flera metoder för att identifiera konstruktionselement i punktmoln har undersökts. Baserat på detta har en metod utvecklats som identifierar plana ytor med regressionsmetoden Random Sample Consensus (RANSAC). Den utvecklade metoden består av en samling algoritmer skrivna i programmeringsspråket Python. Metoden grundar sig i att beräkna skärningspunkter mellan plan samt använder konceptet k-Nearest-Neighbor (k-NN) för att identifiera konstruktionselementens hörnpunkter. Metoden har testats på både simulerade punktmolnsdata och på punktmoln av fysiska broar, där bildinsamling har skett med hjälp av en drönare. Resultatet från de simulerade punktmolnen visade att hörnpunkterna kunde identifieras med en medelavvikelse på 0,13 − 0,34 mm jämfört med de faktiska hörnpunkterna. För ett punktmoln av en rektangulär pelare lyckades algoritmerna identifiera alla relevanta ytor och skapa en rekonstruerad modell med en avvikelse på mindre än 2 % med avseende på dess bredd och längd. Metoden testades även på två punktmoln av riktiga broar. Algoritmerna lyckades identifiera många av de relevanta ytorna, men geometriernas komplexitet resulterade i bristfälligt rekonstruerade modeller.
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Burwell, Claire Leonora. "The effect of 2D vs. 3D visualisation on lidar point cloud analysis tasks." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37950.
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The exploitation of human depth perception is not uncommon in visual analysis of data; medical imagery and geological analysis already rely on stereoscopic 3D visualisation. In contrast, 3D scans of the environment are usually represented on a flat, 2D computer screen, although there is potential to take advantage of both (a) the spatial depth that is offered by the point cloud data, and (b) our ability to see stereoscopically. This study explores whether a stereo 3D analysis environment would add value to visual lidar tasks, compared to the standard 2D display. Forty-six volunteers, all with good stereovision and varying lidar knowledge, viewed lidar data in either 2D or in 3D, on a 4m x 2.4m screen. The first task required 2D and 3D measurement of linear lengths of a planar and a volumetric feature, using an interaction device for point selection. Overall, there was no significant difference in the spread of 2D and 3D measurement distributions for both of the measured features. The second task required interpretation of ten features from individual points. These were highlighted across two areas of interest - a flat, suburban area and a valley slope with a mixture of features. No classification categories were offered to the participant and answers were expressed verbally. Two of the ten features (chimney and cliff-face) were interpreted with a better degree of accuracy using the 3D method and the remaining features had no difference in 2D and 3D accuracy. Using the experiment’s data processing and visualisation approaches, results suggest that stereo 3D perception of lidar data does not add value to manual linear measurement. The interpretation results indicate that immersive stereo 3D visualisation does improve the accuracy of manual point cloud classification for certain features. The findings contribute to wider discussions in lidar processing, geovisualisation, and applied psychology.
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Kudryavtsev, Andrey. "3D Reconstruction in Scanning Electron Microscope : from image acquisition to dense point cloud." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCD050/document.
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L’objectif de ce travail est d’obtenir un modèle 3D d’un objet à partir d’une série d’images prisesavec un Microscope Electronique à Balayage (MEB). Pour cela, nous utilisons la technique dereconstruction 3D qui est une application bien connue du domaine de vision par ordinateur.Cependant, en raison des spécificités de la formation d’images dans le MEB et dans la microscopieen général, les techniques existantes ne peuvent pas être appliquées aux images MEB. Lesprincipales raisons à cela sont la projection parallèle et les problèmes d’étalonnage de MEB entant que caméra. Ainsi, dans ce travail, nous avons développé un nouvel algorithme permettant deréaliser une reconstruction 3D dans le MEB tout en prenant en compte ces difficultés. De plus,comme la reconstruction est obtenue par auto-étalonnage de la caméra, l’utilisation des mires n’estplus requise. La sortie finale des techniques présentées est un nuage de points dense, pouvant donccontenir des millions de points, correspondant à la surface de l’objetThe goal of this work is to obtain a 3D model of an object from its multiple views acquired withScanning Electron Microscope (SEM). For this, the technique of 3D reconstruction is used which isa well known application of computer vision. However, due to the specificities of image formation inSEM, and in microscale in general, the existing techniques are not applicable to the SEM images. Themain reasons for that are the parallel projection and the problems of SEM calibration as a camera.As a result, in this work we developed a new algorithm allowing to achieve 3D reconstruction in SEMwhile taking into account these issues. Moreover, as the reconstruction is obtained through cameraautocalibration, there is no need in calibration object. The final output of the presented techniques isa dense point cloud corresponding to the surface of the object that may contain millions of points
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Nurunnabi, Abdul Awal Md. "Robust statistical approaches for feature extraction in laser scanning 3D point cloud data." Thesis, Curtin University, 2014. http://hdl.handle.net/20.500.11937/543.
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Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction.
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Diskin, Yakov. "Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.
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Grankvist, Ola. "Recognition and Registration of 3D Models in Depth Sensor Data." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131452.
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Object Recognition is the art of localizing predefined objects in image sensor data. In this thesis a depth sensor was used which has the benefit that the 3D pose of the object can be estimated. This has applications in e.g. automatic manufacturing, where a robot picks up parts or tools with a robot arm. This master thesis presents an implementation and an evaluation of a system for object recognition of 3D models in depth sensor data. The system uses several depth images rendered from a 3D model and describes their characteristics using so-called feature descriptors. These are then matched with the descriptors of a scene depth image to find the 3D pose of the model in the scene. The pose estimate is then refined iteratively using a registration method. Different descriptors and registration methods are investigated. One of the main contributions of this thesis is that it compares two different types of descriptors, local and global, which has seen little attention in research. This is done for two different scene scenarios, and for different types of objects and depth sensors. The evaluation shows that global descriptors are fast and robust for objects with a smooth visible surface whereas the local descriptors perform better for larger objects in clutter and occlusion. This thesis also presents a novel global descriptor, the CESF, which is observed to be more robust than other global descriptors. As for the registration methods, the ICP is shown to perform most accurately and ICP point-to-plane more robust.
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Digne, Julie. "Inverse geometry : from the raw point cloud to the 3d surface : theory and algorithms." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00610432.
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Many laser devices acquire directly 3D objects and reconstruct their surface. Nevertheless, the final reconstructed surface is usually smoothed out as a result of the scanner internal de-noising process and the offsets between different scans. This thesis, working on results from high precision scans, adopts the somewhat extreme conservative position, not to loose or alter any raw sample throughout the whole processing pipeline, and to attempt to visualize them. Indeed, it is the only way to discover all surface imperfections (holes, offsets). Furthermore, since high precision data can capture the slightest surface variation, any smoothing and any sub-sampling can incur in the loss of textural detail.The thesis attempts to prove that one can triangulate the raw point cloud with almost no sample loss. It solves the exact visualization problem on large data sets of up to 35 million points made of 300 different scan sweeps and more. Two major problems are addressed. The first one is the orientation of the complete raw point set, an the building of a high precision mesh. The second one is the correction of the tiny scan misalignments which can cause strong high frequency aliasing and hamper completely a direct visualization.The second development of the thesis is a general low-high frequency decomposition algorithm for any point cloud. Thus classic image analysis tools, the level set tree and the MSER representations, are extended to meshes, yielding an intrinsic mesh segmentation method.The underlying mathematical development focuses on an analysis of a half dozen discrete differential operators acting on raw point clouds which have been proposed in the literature. By considering the asymptotic behavior of these operators on a smooth surface, a classification by their underlying curvature operators is obtained.This analysis leads to the development of a discrete operator consistent with the mean curvature motion (the intrinsic heat equation) defining a remarkably simple and robust numerical scale space. By this scale space all of the above mentioned problems (point set orientation, raw point set triangulation, scan merging, segmentation), usually addressed by separated techniques, are solved in a unified framework.
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Cheng, Huaining. "Orthogonal Moment-Based Human Shape Query and Action Recognition from 3D Point Cloud Patches." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452160221.
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Al, Hakim Ezeddin. "3D YOLO: End-to-End 3D Object Detection Using Point Clouds." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234242.
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For safe and reliable driving, it is essential that an autonomous vehicle can accurately perceive the surrounding environment. Modern sensor technologies used for perception, such as LiDAR and RADAR, deliver a large set of 3D measurement points known as a point cloud. There is a huge need to interpret the point cloud data to detect other road users, such as vehicles and pedestrians. Many research studies have proposed image-based models for 2D object detection. This thesis takes it a step further and aims to develop a LiDAR-based 3D object detection model that operates in real-time, with emphasis on autonomous driving scenarios. We propose 3D YOLO, an extension of YOLO (You Only Look Once), which is one of the fastest state-of-the-art 2D object detectors for images. The proposed model takes point cloud data as input and outputs 3D bounding boxes with class scores in real-time. Most of the existing 3D object detectors use hand-crafted features, while our model follows the end-to-end learning fashion, which removes manual feature engineering. 3D YOLO pipeline consists of two networks: (a) Feature Learning Network, an artificial neural network that transforms the input point cloud to a new feature space; (b) 3DNet, a novel convolutional neural network architecture based on YOLO that learns the shape description of the objects. Our experiments on the KITTI dataset shows that the 3D YOLO has high accuracy and outperforms the state-of-the-art LiDAR-based models in efficiency. This makes it a suitable candidate for deployment in autonomous vehicles.För att autonoma fordon ska ha en god uppfattning av sin omgivning används moderna sensorer som LiDAR och RADAR. Dessa genererar en stor mängd 3-dimensionella datapunkter som kallas point clouds. Inom utvecklingen av autonoma fordon finns det ett stort behov av att tolka LiDAR-data samt klassificera medtrafikanter. Ett stort antal studier har gjorts om 2D-objektdetektering som analyserar bilder för att upptäcka fordon, men vi är intresserade av 3D-objektdetektering med hjälp av endast LiDAR data. Därför introducerar vi modellen 3D YOLO, som bygger på YOLO (You Only Look Once), som är en av de snabbaste state-of-the-art modellerna inom 2D-objektdetektering för bilder. 3D YOLO tar in ett point cloud och producerar 3D lådor som markerar de olika objekten samt anger objektets kategori. Vi har tränat och evaluerat modellen med den publika träningsdatan KITTI. Våra resultat visar att 3D YOLO är snabbare än dagens state-of-the-art LiDAR-baserade modeller med en hög träffsäkerhet. Detta gör den till en god kandidat för kunna användas av autonoma fordon.
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Houshiar, Hamidreza [Verfasser], Andreas [Gutachter] Nüchter, and Claus [Gutachter] Brenner. "Documentation and mapping with 3D point cloud processing / Hamidreza Houshiar ; Gutachter: Andreas Nüchter, Claus Brenner." Würzburg : Universität Würzburg, 2017. http://d-nb.info/1127528823/34.
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Houshiar, Hamidreza Verfasser], Andreas [Gutachter] [Nüchter, and Claus [Gutachter] Brenner. "Documentation and mapping with 3D point cloud processing / Hamidreza Houshiar ; Gutachter: Andreas Nüchter, Claus Brenner." Würzburg : Universität Würzburg, 2017. http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144493.
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Fucili, Mattia. "3D object detection from point clouds with dense pose voters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17616/.
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Il riconoscimento di oggetti è sempre stato un compito sfidante per la Computer Vision. Trova applicazione in molti campi, principalmente nell’industria, come ad esempio per permettere ad un robot di trovare gli oggetti da afferrare. Negli ultimi decenni tali compiti hanno trovato nuovi modi di essere raggiunti grazie alla riscoperta delle Reti Neurali, in particolare le Reti Neurali Convoluzionali. Questo tipo di reti ha raggiunto ottimi risultati in molte applicazioni per il riconoscimento e la classificazione degli oggetti. La tendenza, ora, `e quella di utilizzare tali reti anche nell’industria automobilistica per cercare di rendere reale il sogno delle automobili che guidano da sole. Ci sono molti lavori importanti sul riconoscimento delle auto dalle immagini. In questa tesi presentiamo la nostra architettura di Rete Neurale Convoluzionale per il riconoscimento di automobili e la loro posizione nello spazio, utilizzando solo input lidar. Salvando le informazioni riguardanti le bounding box attorno all’auto a livello del punto ci assicura una buona previsione anche in situazioni in cui le automobili sono occluse. I test vengono eseguiti sul dataset più utilizzato per il riconoscimento di automobili e pedoni nelle applicazioni di guida autonoma.
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Schubert, Stefan. "Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-161415.
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Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.
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Megahed, Fadel M. "The Use of Image and Point Cloud Data in Statistical Process Control." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/26511.
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The volume of data acquired in production systems continues to expand. Emerging imaging technologies, such as machine vision systems (MVSs) and 3D surface scanners, diversify the types of data being collected, further pushing data collection beyond discrete dimensional data. These large and diverse datasets increase the challenge of extracting useful information. Unfortunately, industry still relies heavily on traditional quality methods that are limited to fault detection, which fails to consider important diagnostic information needed for process recovery. Modern measurement technologies should spur the transformation of statistical process control (SPC) to provide practitioners with additional diagnostic information. This dissertation focuses on how MVSs and 3D laser scanners can be further utilized to meet that goal. More specifically, this work: 1) reviews image-based control charts while highlighting their advantages and disadvantages; 2) integrates spatiotemporal methods with digital image processing to detect process faults and estimate their location, size, and time of occurrence; and 3) shows how point cloud data (3D laser scans) can be used to detect and locate unknown faults in complex geometries. Overall, the research goal is to create new quality control tools that utilize high density data available in manufacturing environments to generate knowledge that supports decision-making beyond just indicating the existence of a process issue. This allows industrial practitioners to have a rapid process recovery once a process issue has been detected, and consequently reduce the associated downtime.Ph. D.
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Stålberg, Martin. "Reconstruction of trees from 3D point clouds." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-316833.
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The geometrical structure of a tree can consist of thousands, even millions, of branches, twigs and leaves in complex arrangements. The structure contains a lot of useful information and can be used for example to assess a tree's health or calculate parameters such as total wood volume or branch size distribution. Because of the complexity, capturing the structure of an entire tree used to be nearly impossible, but the increased availability and quality of particularly digital cameras and Light Detection and Ranging (LIDAR) instruments is making it increasingly possible. A set of digital images of a tree, or a point cloud of a tree from a LIDAR scan, contains a lot of data, but the information about the tree structure has to be extracted from this data through analysis. This work presents a method of reconstructing 3D models of trees from point clouds. The model is constructed from cylindrical segments which are added one by one. Bayesian inference is used to determine how to optimize the parameters of model segment candidates and whether or not to accept them as part of the model. A Hough transform for finding cylinders in point clouds is presented, and used as a heuristic to guide the proposals of model segment candidates. Previous related works have mainly focused on high density point clouds of sparse trees, whereas the objective of this work was to analyze low resolution point clouds of dense almond trees. The method is evaluated on artificial and real datasets and works rather well on high quality data, but performs poorly on low resolution data with gaps and occlusions.
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Galante, Annamaria. "Studio di CNNs sferiche per l'apprendimento di descrittori locali su Point Cloud." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18680/.
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Nell'ambito della Computer Vision assume sempre maggiore importanza la 3D Computer Vision.
Diversi sono i task e le applicazioni della 3D CV, così come diverse sono le possibili rappresentazioni dei dati.
Molti di questi task richiedono la ricerca di corrispondenze tra due o più scene\oggetti 3D.
Queste corrispondenze vengono individuate tramite il paradigma di Feature Matching, composto da tre step: detection, description, matching.
Le performance della pipe line di feature matching sono strettamente correlate alle tecniche utilizzate in fase di description. La creazione di descriptor compatti, informativi e invarianti alla rotazione è un problema tutt’altro che risolto in letteratura.
Recentemente sono state proposte delle architetture basate su reti convoluzionali sferiche, per il calcolo di descrittori globali da utilizzare in task come shape classification. Questi approcci, grazie alla loro trattazione matematica, permettono di essere equivarianti alla rotazione.
Lo scopo di questo elaborato di tesi è quello di fornire una panoramica dei metodi presenti allo stato dell’arte e proporre un’architettura basata su spherical cnns per apprendere un descrittore locale da usare su nuvole di punti.
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Monnier, Fabrice. "Amélioration de la localisation 3D de données laser terrestre à l'aide de cartes 2D ou modèles 3D." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1114/document.
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Les avancées technologiques dans le domaine informatique (logiciel et matériel) et, en particulier, de la géolocalisation ont permis la démocratisation des modèles numériques. L'arrivée depuis quelques années de véhicules de cartographie mobile a ouvert l'accès à la numérisation 3D mobile terrestre. L'un des avantages de ces nouvelles méthodes d'imagerie de l'environnement urbain est la capacité potentielle de ces systèmes à améliorer les bases de données existantes 2D comme 3D, en particulier leur niveau de détail et la diversité des objets représentés. Les bases de données géographiques sont constituées d'un ensemble de primitives géométriques (généralement des lignes en 2D et des plans ou des triangles en 3D) d'un niveau de détail grossier mais ont l'avantage d'être disponibles sur de vastes zones géographiques. Elles sont issues de la fusion d'informations diverses (anciennes campagnes réalisées manuellement, conception automatisée ou encore hybride) et peuvent donc présenter des erreurs de fabrication. Les systèmes de numérisation mobiles, eux, peuvent acquérir, entre autres, des nuages de points laser. Ces nuages laser garantissent des données d'un niveau de détail très fin pouvant aller jusqu'à plusieurs points au centimètre carré. Acquérir des nuages de points laser présente toutefois des inconvénients :- une quantité de données importante sur de faibles étendues géographiques posant des problèmes de stockage et de traitements pouvant aller jusqu'à plusieurs Téraoctet lors de campagnes d'acquisition importantes- des difficultés d'acquisition inhérentes au fait d'imager l'environnement depuis le sol. Les systèmes de numérisation mobiles présentent eux aussi des limites : en milieu urbain, le signal GPS nécessaire au bon géoréférencement des données peut être perturbé par les multi-trajets voire même stoppé lors de phénomènes de masquage GPS liés à la réduction de la portion de ciel visible pour capter assez de satellites pour en déduire une position spatiale. Améliorer les bases de données existantes grâce aux données acquises par un véhicule de numérisation mobile nécessite une mise en cohérence des deux ensembles. L'objectif principal de ce manuscrit est donc de mettre en place une chaîne de traitements automatique permettant de recaler bases de données géographiques et nuages de points laser terrestre (provenant de véhicules de cartographies mobiles) de la manière la plus fiable possible. Le recalage peut se réaliser de manière différentes. Dans ce manuscrit, nous avons développé une méthode permettant de recaler des nuages laser sur des bases de données, notamment, par la définition d'un modèle de dérive particulièrement adapté aux dérives non-linéaires de ces données mobiles. Nous avons également développé une méthode capable d'utiliser de l'information sémantique pour recaler des bases de données sur des nuages laser mobiles. Les différentes optimisations effectuées sur notre approche nous permettent de recaler des données rapidement pour une approche post-traitements, ce qui permet d'ouvrir l'approche à la gestion de grands volumes de données (milliards de points laser et milliers de primitives géométriques).Le problème du recalage conjoint a été abordé. Notre chaîne de traitements a été testée sur des données simulées et des données réelles provenant de différentes missions effectuées par l'IGNTechnological advances in computer science (software and hardware) and particularly, GPS localization made digital models accessible to all people. In recent years, mobile mapping systems has enabled large scale mobile 3D scanning. One advantage of this technology for the urban environment is the potential ability to improve existing 2D or 3D database, especially their level of detail and variety of represented objects. Geographic database consist of a set of geometric primitives (generally 2D lines and plans or triangles in 3D) with a coarse level of detail but with the advantage of being available over wide geographical areas. They come from the fusion of various information (old campaigns performed manually, automated or hybrid design) wich may lead to manufacturing errors. The mobile mapping systems can acquire laser point clouds. These point clouds guarantee a fine level of detail up to more than one points per square centimeter. But there are some disavantages :- a large amount of data on small geographic areas that may cause problems for storage and treatment of up to several Terabyte during major acquisition,- the inherent acquisition difficulties to image the environment from the ground. In urban areas, the GPS signal required for proper georeferencing data can be disturbed by multipath or even stopped when GPS masking phenomena related to the reduction of the portion of the visible sky to capture enough satellites to find a good localization. Improve existing databases through these dataset acquired by a mobile mapping system requires alignment of these two sets. The main objective of this manuscript is to establish a pipeline of automatic processes to register these datasets together in the most reliable manner. Co-registration this data can be done in different ways. In this manuscript we have focused our work on the registration of mobile laser point cloud on geographical database by using a drift model suitable for the non rigid drift of these kind of mobile data. We have also developped a method to register geographical database containing semantics on mobile point cloud. The different optimization step performed on our methods allows to register the data fast enough for post-processing pipeline, which allows the management of large volumes of data (billions of laser points and thousands geometric primitives). We have also discussed on the problem of joint deformation. Our methods have been tested on simulated data and real data from different mission performed by IGN
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Westling, Fredrik Anders. "Pruning of Tree Crops through 3D Reconstruction and Light Simulation using Mobile LiDAR." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27427.
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Consistent sunlight access is critical when growing fruit crops, and therefore pruning is a vital operation for tree management as it can be used for controlling shading within and between trees. This thesis focuses on using Light Detection And Ranging (LiDAR) to understand and improve the light distribution of fruit trees. To enable commercial applications, the tools developed aim to provide insights on every individual tree at whole orchard scale.
Since acquisition and labelling of 3D data is difficult at a large scale, a system is developed for simulating LiDAR scans of tree crops for development and validation of techniques using infinite, perfectly-labelled datasets. Furthermore, processing scans at a large scale require rapid and relatively low-cost solutions, but many existing methods for point cloud analysis require a priori information or expensive high quality LiDAR scans. New tools are presented for structural analysis of noisy mobile LiDAR scans using a novel graph-search approach which can operate on unstructured point clouds with significant overlap between trees.
The light available to trees is important for predicting future growth and crop yields as well as making pruning decisions, but many measurement techniques cannot provide branch-level analysis, or are difficult to apply on a large scale. Using mobile LiDAR, which can easily capture large areas, a method is developed to estimate the light available throughout the canopy. A study is then performed to demonstrate the viability of this approach to replace traditional agronomic methods, enabling large-scale adoption.
The main contribution of this thesis is a novel framework for suggesting pruning decisions to improve light availability of individual trees. A full-tree quality metric is proposed and branch-scale light information identifies underexposed areas of the tree to suggest branches whose removal will improve the light distribution. Simulated tree scans are then used to validate a technique for estimating matter removed from the point cloud given specific pruning decisions, and this is used to quantify the improvement of real tree scans. The findings of this iv ABSTRACT v thesis demonstrate the value and application of mobile LiDAR in tree crops, and the tools developed through this work promise usefulness in scientific and commercial contexts.
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Po-ChiHsu and 許伯祺. "3D Building Model Retrieval for Point Cloud Modeling." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/76872673208853466398.
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Lai, Hung-Ruei, and 賴泓瑞. "Template-based 3D Point Cloud Modeling for City Buildings." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60626826866060441873.
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碩士國立成功大學
測量及空間資訊學系碩博士班
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Digital scanning devices such as LiDAR (Light Detection and Ranging) have recently become affordable and available. They are capable of acquiring high-accuracy and high-resolution point clouds. Thus, the techniques for point cloud modeling have received increasingly attentions in the last decade. As the approaches reconstruct the point clouds, they face a common problem: how to handle point clouds with inherent noises. Moreover, it will be especially challenge in handing point clouds that contains sharp features, e.g., city buildings. In the thesis, a novel template-based modeling approach for 3D point clouds sampled from unknown city buildings is introduced. A hierarchy algebraic template, comprising of three types of primitive geometries (that is, plane, sphere, and cylinder), is used to fit point clouds. The algebraic template is organized in a hierarchical manner. The first-level, i.e., the lowest-level, consists of the primitive geometries which are represented in algebra form. These primitive geometries are merged into 3D objects with simple shapes in the next level. These 3D objects are further joined to form the final template model in the last level. After the point clouds are partitioned (using RANSAC algorithm) into several geometric sets, the constructed template model is used to fit them. The point cloud fitting is archived by solving a least-square linear system instead of solving a non-linear one, making the approach efficient and robust in the modeling. In addition, some geometric constraints on the primitive geometries are added in the point cloud fitting (i.e., least-square linear system) for the purpose of improving modeling quality. The experimental results show that the approach is better, in terms of sharp feature fitting and noise withstanding, than the approaches based on implicit surfaces. In addition, comparing to the general least-square fitting approaches, the template-based fitting with geometry constraints improves modeling quality with respect to human visual system.