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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.
Pełny tekst źródłaDiskin, Yakov. "Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models". University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.
Pełny tekst źródłaMorell, Vicente. "Contributions to 3D Data Registration and Representation". Doctoral thesis, Universidad de Alicante, 2014. http://hdl.handle.net/10045/42364.
Pełny tekst źródłaOrts-Escolano, Sergio. "A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs". Doctoral thesis, Universidad de Alicante, 2013. http://hdl.handle.net/10045/36484.
Pełny tekst źródłaZhao, Yongheng. "3D feature representations for visual perception and geometric shape understanding". Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424787.
Pełny tekst źródłaKonradsson, Albin, i Gustav Bohman. "3D Instance Segmentation of Cluttered Scenes : A Comparative Study of 3D Data Representations". Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177598.
Pełny tekst źródłaCao, Chao. "Compression d'objets 3D représentés par nuages de points". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS015.
Pełny tekst źródłaWith the rapid growth of multimedia content, 3D objects are becoming more and more popular. Most of the time, they are modeled as complex polygonal meshes or dense point clouds, providing immersive experiences in different industrial and consumer multimedia applications. The point cloud, which is easier to acquire than mesh and is widely applicable, has raised many interests in both the academic and commercial worlds.A point cloud is a set of points with different properties such as their geometrical locations and the associated attributes (e.g., color, material properties, etc.). The number of the points within a point cloud can range from a thousand, to constitute simple 3D objects, up to billions, to realistically represent complex 3D scenes. Such huge amounts of data bring great technological challenges in terms of transmission, processing, and storage of point clouds.In recent years, numerous research works focused their efforts on the compression of meshes, while less was addressed for point clouds. We have identified two main approaches in the literature: a purely geometric one based on octree decomposition, and a hybrid one based on both geometry and video coding. The first approach can provide accurate 3D geometry information but contains weak temporal consistency. The second one can efficiently remove the temporal redundancy yet a decrease of geometrical precision can be observed after the projection. Thus, the tradeoff between compression efficiency and accurate prediction needs to be optimized.We focused on exploring the temporal correlations between dynamic dense point clouds. We proposed different approaches to improve the compression performance of the MPEG (Moving Picture Experts Group) V-PCC (Video-based Point Cloud Compression) test model, which provides state-of-the-art compression on dynamic dense point clouds.First, an octree-based adaptive segmentation is proposed to cluster the points with different motion amplitudes into 3D cubes. Then, motion estimation is applied to these cubes using affine transformation. Gains in terms of rate-distortion (RD) performance have been observed in sequences with relatively low motion amplitudes. However, the cost of building an octree for the dense point cloud remains expensive while the resulting octree structures contain poor temporal consistency for the sequences with higher motion amplitudes.An anatomical structure is then proposed to model the motion of the point clouds representing humanoids more inherently. With the help of 2D pose estimation tools, the motion is estimated from 14 anatomical segments using affine transformation.Moreover, we propose a novel solution for color prediction and discuss the residual coding from prediction. It is shown that instead of encoding redundant texture information, it is more valuable to code the residuals, which leads to a better RD performance.Although our contributions have improved the performances of the V-PCC test models, the temporal compression of dynamic point clouds remains a highly challenging task. Due to the limitations of the current acquisition technology, the acquired point clouds can be noisy in both geometry and attribute domains, which makes it challenging to achieve accurate motion estimation. In future studies, the technologies used for 3D meshes may be exploited and adapted to provide temporal-consistent connectivity information between dynamic 3D point clouds
Hejl, Zdeněk. "Rekonstrukce 3D scény z obrazových dat". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236495.
Pełny tekst źródłaSmith, 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.
Pełny tekst źródłaRoure, Garcia Ferran. "Tools for 3D point cloud registration". Doctoral thesis, Universitat de Girona, 2017. http://hdl.handle.net/10803/403345.
Pełny tekst źródłaEn 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
Tarcin, Serkan. "Fast Feature Extraction From 3d Point Cloud". Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615659/index.pdf.
Pełny tekst źródłaForsman, Mona. "Point cloud densification". Thesis, Umeå universitet, Institutionen för fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39980.
Pełny tekst źródłaGujar, Sanket. "Pointwise and Instance Segmentation for 3D Point Cloud". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1290.
Pełny tekst źródłaChen, Chen. "Semantics Augmented Point Cloud Sampling for 3D Object Detection". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26956.
Pełny tekst źródłaDey, Emon Kumar. "Effective 3D Building Extraction from Aerial Point Cloud Data". Thesis, Griffith University, 2022. http://hdl.handle.net/10072/413311.
Pełny tekst źródłaThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
Eckart, Benjamin. "Compact Generative Models of Point Cloud Data for 3D Perception". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1089.
Pełny tekst źródłaOropallo, 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.
Pełny tekst źródłaVarious 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.
Lev, Hoang Justin. "A Study of 3D Point Cloud Features for Shape Retrieval". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM040.
Pełny tekst źródłaWith 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
Kulkarni, Amey S. "Motion Segmentation for Autonomous Robots Using 3D Point Cloud Data". Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1370.
Pełny tekst źródłaAronsson, Oskar, i 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.
Pełny tekst źródłaBesiktning 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.
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.
Pełny tekst źródłaDownham, 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.
Pełny tekst źródłaTrowbridge, 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.
Pełny tekst źródłaA 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.
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.
Pełny tekst źródłaBlahož, 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.
Pełny tekst źródłaBurwell, 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.
Pełny tekst źródłaKudryavtsev, 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.
Pełny tekst źródłaThe 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
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.
Pełny tekst źródłaGrankvist, 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.
Pełny tekst źródłaDigne, 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.
Pełny tekst źródłaCheng, 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.
Pełny tekst źródłaAl, 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.
Pełny tekst źródłaFör 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.
IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.
Pełny tekst źródłaDahlin, Johan. "3D Modeling of Indoor Environments". Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93999.
Pełny tekst źródłaHammoudi, 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.
Pełny tekst źródłaHoushiar, Hamidreza [Verfasser], Andreas [Gutachter] Nüchter i 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.
Pełny tekst źródłaHoushiar, Hamidreza Verfasser], Andreas [Gutachter] [Nüchter i 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.
Pełny tekst źródłaFucili, 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/.
Pełny tekst źródłaSchubert, 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.
Pełny tekst źródłaMegahed, Fadel M. "The Use of Image and Point Cloud Data in Statistical Process Control". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/26511.
Pełny tekst źródłaPh. D.
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.
Pełny tekst źródłaMonnier, 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.
Pełny tekst źródłaTechnological 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
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/.
Pełny tekst źródłaWestling, 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.
Pełny tekst źródłaAnadon, Leon Hector. "3D Shape Detection for Augmented Reality". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231727.
Pełny tekst źródła2D-objektigenkänning har i tidigare arbeten uppvisat exceptionella resultat. Dessa modeller gör det dock inte möjligt att erhålla rumsinformation, så som föremåls position och information om vad föremålen är. Sådan kunskap kan leda till förbättringar inom flera områden så som förstärkt verklighet, så att virtuella karaktärer mer realistiskt kan interagera med miljön, samt för självstyrande bilar, så att de kan fatta bättre beslut och veta var objekt är i ett 3D-utrymme. Detta arbete visar att det är möjligt att modellera täckande rätblock med semantiska etiketter för 3D-objektdetektering, samt underliggande komponenter för 3D-formigenkänning, från flera objekt i en inomhusmiljö med en algoritm som verkar på en RGB-bild och dess 3D-information. Modellen konstrueras med djupa neurala nätverk med nya arkitekturer för Point Cloud-representationsextraktion. Den använder en unik representationsvektor som kan representera det latenta utrymmet i objektet som modellerar dess form, position, storlek och orientering för komplett träning med flera uppgifter, med obalanserade dataset. Den körs i realtid (5 bilder per sekund) i realtidsvideo. Metoden utvärderas med NYU Depth Dataset V2 med Genomsnittlig Precision för objektdetektering, 3D-Skärning över Union, samt avstånd mellan ytorna för 3D-form. Resultaten bekräftar att det är möjligt att använda en delad representationsvektor för mer än en prediktionsuppgift, och generaliserar för föremål som inte observerats under träningsprocessen. Den uppnår toppresultat för 3D-objektdetektering samt 3D-form-prediktion för NYU Depth Dataset V2. Kvalitativa resultat baserade på särskilt anskaffade data visar potential inom navigering i en verklig inomhusmiljö, samt kollision mellan animationer och detekterade objekt, vilka kan förbättra interaktonen mellan karaktär och miljö inom förstärkt verklighet-applikationer.
Oboňová, Veronika. "Využití laserového skenování pro 3D modelování". Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2017. http://www.nusl.cz/ntk/nusl-390221.
Pełny tekst źródłaBose, 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.
Znajdź pełny tekst źródłaPereira, Nícolas Silva. "Cloud Partitioning Iterative Closest Point (CP-ICP): um estudo comparativo para registro de nuvens de pontos 3D". reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/22971.
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In relation to the scientific and technologic evolution of equipment such as cameras and image sensors, the computer vision presents itself more and more as a consolidated engineering solution to issues in diverse fields. Together with it, due to the 3D image sensors dissemination, the improvement and optimization of techniques that deals with 3D point clouds registration, such as the classic algorithm Iterative Closest Point (ICP), appear as fundamental on solving problems such as collision avoidance and occlusion treatment. In this context, this work proposes a sampling technique to be used prior to the ICP algorithm. The proposed method is compared to other five variations of sampling techniques based on three criteria: RMSE (root mean squared error), based also on an Euler angles analysis and an autoral criterion based on structural similarity index (SSIM). The experiments were developed on four distincts 3D models from two databases, and shows that the proposed technique achieves a more accurate point cloud registration in a smaller time than the other techniques.
Com a evolução científica e tecnológica de equipamentos como câmeras e sensores de imagens, a visão computacional se mostra cada vez mais consolidada como solução de engenharia para problemas das mais diversas áreas. Associando isto com a disseminação dos sensores de imagens 3D, o aperfeiçoamento e a otimização de técnicas que lidam com o registro de nuvens de pontos 3D, como o algoritmo clássico Iterative Closest Point (ICP), se mostram fundamentais na resolução de problemas como desvio de colisão e tratamento de oclusão. Nesse contexto, este trabalho propõe um técnica de amostragem a ser utilizada previamente ao algoritmo ICP. O método proposto é comparado com outras cinco varições de amostragem a partir de três critérios: RMSE (root mean squared error ), a partir de uma análise de ângulos de Euler e uma métrica autoral baseada no índice de structural similarity (SSIM). Os experimentos foram desenvolvidos em quatro modelos 3D distintos vindos de dois diferentes databases, e revelaram que a abordagem apresentada alcançou um registro de nuvens mais acuraz num tempo menor que as outras técnicas.
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.
Pełny tekst źródłaYUAN, ZEHUI. "Plane-based 3D Mapping for Structured Indoor Environment". Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506288.
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