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Cao, 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
Thomas, Hugues. "Apprentissage de nouvelles représentations pour la sémantisation de nuages de points 3D". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM048/document.
Pełny tekst źródłaIn the recent years, new technologies have allowed the acquisition of large and precise 3D scenes as point clouds. They have opened up new applications like self-driving vehicles or infrastructure monitoring that rely on efficient large scale point cloud processing. Convolutional deep learning methods cannot be directly used with point clouds. In the case of images, convolutional filters brought the ability to learn new representations, which were previously hand-crafted in older computer vision methods. Following the same line of thought, we present in this thesis a study of hand-crafted representations previously used for point cloud processing. We propose several contributions, to serve as basis for the design of a new convolutional representation for point cloud processing. They include a new definition of multiscale radius neighborhood, a comparison with multiscale k-nearest neighbors, a new active learning strategy, the semantic segmentation of large scale point clouds, and a study of the influence of density in multiscale representations. Following these contributions, we introduce the Kernel Point Convolution (KPConv), which uses radius neighborhoods and a set of kernel points to play the role of the kernel pixels in image convolution. Our convolutional networks outperform state-of-the-art semantic segmentation approaches in almost any situation. In addition to these strong results, we designed KPConv with a great flexibility and a deformable version. To conclude our argumentation, we propose several insights on the representations that our method is able to learn
Roynard, Xavier. "Sémantisation à la volée de nuages de points 3D acquis par systèmes embarqués". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM078.
Pełny tekst źródłaThis thesis is at the confluence of two worlds in rapid growth: autonomous cars and artificial intelligence (especially deep learning). As the first takes advantage of the second, autonomous vehicles are increasingly using deep learning methods to analyze the data produced by its various sensors (including LiDARs) and to make decisions. While deep learning methods have revolutionized image analysis (in classification and segmentation for example), they do not produce such spectacular results on 3D point clouds. This is particularly true because the datasets of annotated 3D point clouds are rare and of moderate quality. This thesis therefore presents a new dataset developed by mobile acquisition to produce enough data and annotated by hand to ensure a good quality of segmentation. In addition, these datasets are inherently unbalanced in number of samples per class and contain many redundant samples, so a sampling method adapted to these datasets is proposed. Another problem encountered when trying to classify a point from its neighbourhood as a voxel grid is the compromise between a fine discretization step (for accurately describing the surface adjacent to the point) and a large grid (to look for context a little further away). We therefore also propose network methods that take advantage of multi-scale neighbourhoods. These methods achieve the state of the art of point classification methods on public benchmarks. Finally, to respect the constraints imposed by embedded systems (real-time processing and low computing power), we present a method that allows convolutional layers to be applied only where there is information to be processed
Boudjemaï, Farid. "Reconstruction de surfaces d'objets 3D à partir de nuages de points par réseaux de neurones 3D-SOM". Lille 1, 2006. https://ori-nuxeo.univ-lille1.fr/nuxeo/site/esupversions/bcedde4b-f138-4193-8cec-20a49de14358.
Pełny tekst źródłaDeschaud, Jean-Emmanuel. "Traitements de nuages de points denses et modélisation 3D d'environnements par système mobile LiDAR/Caméra". Phd thesis, Paris, ENMP, 2010. https://pastel.hal.science/pastel-00580384.
Pełny tekst źródłaThe Robotics Laboratory CAOR from Mines ParisTech has developed a technique for 3D scanning of outdoor environments, using a mobile system called LARA3D. This is a car with location sensors (GPS, inertial unit) and modeling sensors (lidars and cameras). This device can build 3D point clouds and geo-referenced images of the environment. As part of this thesis, we have developed a processing chain of these point clouds and images for generating photo-realistic 3D models. This modeling chain has many processing steps with in order : normal computation, denoising, planar area segmentation, triangulation, simplification and texturing. We have applied our modeling method on synthetic data and on real data acquired through project TerraNumerica (in urban environment) and project Divas (in road environment)
Deschaud, Jean-Emmanuel. "Traitements de nuages de points denses et modélisation 3D d'environnements par système mobile LiDAR/Caméra". Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00580384.
Pełny tekst źródłaItier, Vincent. "Nouvelles méthodes de synchronisation de nuages de points 3D pour l'insertion de données cachées". Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS017/document.
Pełny tekst źródłaThis thesis addresses issues relating to the protection of 3D object meshes. For instance, these objects can be created using CAD tool developed by the company STRATEGIES. In an industrial context, 3D meshes creators need to have tools in order to verify meshes integrity, or check permission for 3D printing for example.In this context we study data hiding on 3D meshes. This approach allows us to insert information in a secure and imperceptible way in a mesh. This may be an identifier, a meta-information or a third-party content, for instance, in order to transmit secretly a texture. Data hiding can address these problems by adjusting the trade-off between capacity, imperceptibility and robustness. Generally, data hiding methods consist of two stages, the synchronization and the embedding. The synchronization stage consists of finding and ordering available components for insertion. One of the main challenges is to propose an effective synchronization method that defines an order on mesh components. In our work, we propose to use mesh vertices, specifically their geometric representation in space, as basic components for synchronization and embedding. We present three new synchronisation methods based on the construction of a Hamiltonian path in a vertex cloud. Two of these methods jointly perform the synchronization stage and the embedding stage. This is possible thanks to two new high-capacity embedding methods (from 3 to 24 bits per vertex) that rely on coordinates quantization. In this work we also highlight the constraints of this kind of synchronization. We analyze the different approaches proposed with several experimental studies. Our work is assessed on various criteria including the capacity and imperceptibility of the embedding method. We also pay attention to security aspects of the proposed methods
Horache, Sofiane. "Comparaison de motifs sur des nuages de points 3D et application sur des monnaies et objets celtiques". Thesis, Université Paris sciences et lettres, 2022. https://pastel.archives-ouvertes.fr/tel-03789632.
Pełny tekst źródłaClustering coins according to their die is a problem that has many applications in numismatics. This clustering is crucial for understanding the economic history of tribes (especially for tribes for whom few written records exist, such as the Celts). It is a difficult task, requiring a lot of times and expertises. However, there is very little work that has been done on coin die identification.This thesis project aims at proposing an automatic tool to know if two patterns have been impressed with the same tool, especially to know if two coins have been struck with the same die. Based on deep learning-based registration algorithms, the proposed method has allowed us to classify a hoard of a thousand Riedone coins dating from the 2nd century BC. This treasure allowed us to build an annotated dataset of 3D acquisitions called Riedones3D. Riedones3D is useful for Celtic coin specialists, but also for the computer vision community to develop new coin die recognition algorithms. Rigorous evaluations on Riedones3D and on other Celtic works show the interest of the proposed method. Indeed, it can be adapted to unknown patterns. Finally, we propose a new registration algorithm that can be adapted to any type of sensor. Thanks to this algorithm, it is potentially possible for a specialist to use faster or less expensive sensors to acquire coins or engraved patterns
El, Sayed Abdul Rahman. "Traitement des objets 3D et images par les méthodes numériques sur graphes". Thesis, Normandie, 2018. http://www.theses.fr/2018NORMLH19/document.
Pełny tekst źródłaSkin detection involves detecting pixels corresponding to human skin in a color image. The faces constitute a category of stimulus important by the wealth of information that they convey because before recognizing any person it is essential to locate and recognize his face. Most security and biometrics applications rely on the detection of skin regions such as face detection, 3D adult object filtering, and gesture recognition. In addition, saliency detection of 3D mesh is an important pretreatment phase for many computer vision applications. 3D segmentation based on salient regions has been widely used in many computer vision applications such as 3D shape matching, object alignments, 3D point-point smoothing, searching images on the web, image indexing by content, video segmentation and face detection and recognition. The detection of skin is a very difficult task for various reasons generally related to the variability of the shape and the color to be detected (different hues from one person to another, orientation and different sizes, lighting conditions) and especially for images from the web captured under different light conditions. There are several known approaches to skin detection: approaches based on geometry and feature extraction, motion-based approaches (background subtraction (SAP), difference between two consecutive images, optical flow calculation) and color-based approaches. In this thesis, we propose numerical optimization methods for the detection of skins color and salient regions on 3D meshes and 3D point clouds using a weighted graph. Based on these methods, we provide 3D face detection approaches using Linear Programming and Data Mining. In addition, we adapted our proposed methods to solve the problem of simplifying 3D point clouds and matching 3D objects. In addition, we show the robustness and efficiency of our proposed methods through different experimental results. Finally, we show the stability and robustness of our methods with respect to noise
Lejemble, Thibault. "Analyse multi-échelle de nuage de points". Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30184.
Pełny tekst źródła3D acquisition techniques like photogrammetry and laser scanning are commonly used in numerous fields such as reverse engineering, archeology, robotics and urban planning. The main objective is to get virtual versions of real objects in order to visualize, analyze and process them easily. Acquisition techniques become more and more powerful and affordable which creates important needs to process efficiently the resulting various and massive 3D data. Data are usually obtained in the form of unstructured 3D point cloud sampling the scanned surface. Traditional signal processing methods cannot be directly applied due to the lack of spatial parametrization. Points are only represented by their 3D coordinates without any particular order. This thesis focuses on the notion of scale of analysis defined by the size of the neighborhood used to locally characterize the point-sampled surface. The analysis at different scales enables to consider various shapes which increases the analysis pertinence and the robustness to acquired data imperfections. We first present some theoretical and practical results on curvature estimation adapted to a multi-scale and multi-resolution representation of point clouds. They are used to develop multi-scale algorithms for the recognition of planar and anisotropic shapes such as cylinders and feature curves. Finally, we propose to compute a global 2D parametrization of the underlying surface directly from the 3D unstructured point cloud
Jubert, Manon. "Algorithme de planification de numérisation et d’alignement de nuages de points 3D pour le contrôle in-situ de pièces mécaniques en cours d’usinage". Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0233.
Pełny tekst źródłaIn this thesis work, we deal with the problem of scan planning and we also address the problem of 3D point clouds alignment. These problems can be studied and globalized to meet the needs of many fields of study. In this manuscript, we answer the following question: how to automatically control a mechanical part during machining? Or more precisely: how to obtain a good representation of the part so as to check its good conformity during the machining process? The first contribution to this study is the development of an automatic algorithm for planning the digitization of the part according to the controls to be carried out on it. The approach allows to adapt to any optical digitizing device. In our study, we show the results of this method on several fringe projection optical sensors and on several industrial parts. The proposed method is general and allows to adapt to any industrial environment in which the part to be controlled is positioned. The second contribution focuses on the specific case of data from optical sensors, i.e. 3D point clouds. From the scanning plan, we develop a strategy for aligning point clouds between them. We address the problem of cases where the alignment of point clouds is not possible and try to solve this problem. Finally, we show several industrial applications of these algorithms, and we study the precision of the methods on these cases. We propose several openings on the continuation of this work, in particular on the specific cases of non-alignable parts or on large parts. Ideas for improvement and robustification of the algorithms are discussed
Soma, Maxime. "Estimation de la distribution spatiale de surface et de biomasse foliaires de couverts forestiers méditerranéens à partir de nuages de points acquis par un LIDAR terrestre". Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0111.
Pełny tekst źródłaTo better understand functioning of forest ecosystems at fine scale, ecophysiological model attempt to include energy and material fluxes. Such exchanges depend on the distribution of vegetation. Hence, these models require a tridimensional (3D) description of vegetation structure, at a level of detail which can only be retrieve with remote sensing at large scale. Terrestrial LiDAR (Light Detection And Ranging) have a great potential to provide 3D description of vegetation elements in canopy. Previous studies established promising relations between the point density and quantity of vegetation. This work develop these statistical methods, focusing on source of errors. Systematic biases are corrected at branch, tree and plot scales. This study relies on both numerical simulations and field experiments. First, we test estimators on branches in laboratory conditions. On this natural vegetation, estimators are sensitive to voxel size and distance from instrument with phase-shift LiDAR. Developed corrections from this branch experiment are valid at tree scale. However, difficulties arising from sampling limitations due to occlusion and instrument sampling pattern cause negative biases in dense areas. Specific investigations are conducted to identify source of errors and to optimize multiscan estimations. A statistical method called LAD-kriging, based on spatial correlation within vegetation, improves local accuracy of estimations and limits underestimations due to occlusion. The tools produced in this work allow to map vegetation at plot scale by providing unbiased estimator of leaf area. Some of these tools are currently implemented within open access Computree software
Macher, Hélène. "Du nuage de points à la maquette numérique de bâtiment : reconstruction 3D semi-automatique de bâtiments existants". Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD006/document.
Pełny tekst źródłaThe creation of an as-built BIM requires the acquisition of the as-is conditions of existing buildings. Terrestrial laser scanning (TLS) is widely used to achieve this goal. Indeed, laser scanners permit to collect information about object geometry in form of point clouds. They provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM process remains currently largely a manual process because of the huge amount of data and because of processes, which are difficult to automate. It is time consuming and error-prone. A key challenge today is thus to automate the process leading to 3D reconstruction of existing buildings from point clouds. The aim of this thesis is to develop a processing chain to extract the maximum amount of information from a building point cloud in order to integrate the result in a BIM software
Ben, salah Imeen. "Extraction d'un graphe de navigabilité à partir d'un nuage de points 3D enrichis". Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR070/document.
Pełny tekst źródłaCameras have become increasingly common in vehicles, smart phones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied : pedestrian detection, line crossing detection, navigation ... Vision-based navigation has reached a certain maturity in recent years through the use of advanced technologies. Vision-based navigation systems have the considerable advantage of being able to directly use the visual information already existing in the environment without having to adapt any element of the infrastructure. In addition, unlike systems using GPS, they can be used outdoors and indoors without any loss of precision. This guarantees the superiority of these systems based on computer vision. A major area of {research currently focuses on mapping, which represents an essential step for navigation. This step generates a problem of memory management quite substantial required by these systems because of the huge amount of information collected by each sensor. Indeed, the memory space required to accommodate the map of a small city is measured in tens of GB or even thousands when one wants to cover large spaces. This makes impossible to integrate this map into a mobile system such as smartphones , cameras embedded in vehicles or robots. The challenge would be to develop new algorithms to minimize the size of the memory needed to operate this navigation system using only computer vision. It's in this context that our project consists in developing a new system able to summarize a3D map resulting from the visual information collected by several sensors. The summary will be a set of spherical views allow to keep the same level of visibility in all directions. It would also guarantee, at a lower cost, a good level of precision and speed during navigation. The summary map of the environment will contain geometric, photometric and semantic information
Guislain, Maximilien. "Traitement joint de nuage de points et d'images pour l'analyse et la visualisation des formes 3D". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1219/document.
Pełny tekst źródłaRecent years saw a rapid development of city digitization technologies. Acquisition campaigns covering entire cities are now performed using LiDAR (Light Detection And Ranging) scanners embedded aboard mobile vehicles. These acquisition campaigns yield point clouds, composed of millions of points, representing the buildings and the streets, and may also contain a set of images of the scene. The subject developed here is the improvement of the point cloud using the information contained in the camera images. This thesis introduces several contributions to this joint improvement. The position and orientation of acquired images are usually estimated using devices embedded with the LiDAR scanner, even if this information is inaccurate. To obtain the precise registration of an image on a point cloud, we propose a two-step algorithm which uses both Mutual Information and Histograms of Oriented Gradients. The proposed method yields an accurate camera pose, even when the initial estimations are far from the real position and orientation. Once the images have been correctly registered, it is possible to use them to color each point of the cloud while using the variability of the point of view. This is done by minimizing an energy considering the different colors associated with a point and the potential colors of its neighbors. Illumination changes can also change the color assigned to a point. Notably, this color can be affected by cast shadows. These cast shadows are changing with the sun position, it is therefore necessary to detect and correct them. We propose a new method that analyzes the joint variation of the reflectance value obtained by the LiDAR and the color of the points. By detecting enough interfaces between shadow and light, we can characterize the luminance of the scene and to remove the cast shadows. The last point developed in this thesis is the densification of a point cloud. Indeed, the local density of a point cloud varies and is sometimes insufficient in certain areas. We propose a directly applicable approach to increase the density of a point cloud using multiple images
Jaritz, Maximilian. "2D-3D scene understanding for autonomous driving". Thesis, Université Paris sciences et lettres, 2020. https://pastel.archives-ouvertes.fr/tel-02921424.
Pełny tekst źródłaIn this thesis, we address the challenges of label scarcity and fusion of heterogeneous 3D point clouds and 2D images. We adopt the strategy of end-to-end race driving where a neural network is trained to directly map sensor input (camera image) to control output, which makes this strategy independent from annotations in the visual domain. We employ deep reinforcement learning where the algorithm learns from reward by interaction with a realistic simulator. We propose new training strategies and reward functions for better driving and faster convergence. However, training time is still very long which is why we focus on perception to study point cloud and image fusion in the remainder of this thesis. We propose two different methods for 2D-3D fusion. First, we project 3D LiDAR point clouds into 2D image space, resulting in sparse depth maps. We propose a novel encoder-decoder architecture to fuse dense RGB and sparse depth for the task of depth completion that enhances point cloud resolution to image level. Second, we fuse directly in 3D space to prevent information loss through projection. Therefore, we compute image features with a 2D CNN of multiple views and then lift them all to a global 3D point cloud for fusion, followed by a point-based network to predict 3D semantic labels. Building on this work, we introduce the more difficult novel task of cross-modal unsupervised domain adaptation, where one is provided with multi-modal data in a labeled source and an unlabeled target dataset. We propose to perform 2D-3D cross-modal learning via mutual mimicking between image and point cloud networks to address the source-target domain shift. We further showcase that our method is complementary to the existing uni-modal technique of pseudo-labeling
Ben, Abdallah Hamdi. "Inspection d'assemblages aéronautiques par vision 2D/3D en exploitant la maquette numérique et la pose estimée en temps réel Three-dimensional point cloud analysis for automatic inspection of complex aeronautical mechanical assemblies Automatic inspection of aeronautical mechanical assemblies by matching the 3D CAD model and real 2D images". Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2020. http://www.theses.fr/2020EMAC0001.
Pełny tekst źródłaThis thesis makes part of a research aimed towards innovative digital tools for the service of what is commonly referred to as Factory of the Future. Our work was conducted in the scope of the joint research laboratory "Inspection 4.0" founded by IMT Mines Albi/ICA and the company DIOTA specialized in the development of numerical tools for Industry 4.0. In the thesis, we were interested in the development of systems exploiting 2D images or (and) 3D point clouds for the automatic inspection of complex aeronautical mechanical assemblies (typically an aircraft engine). The CAD (Computer Aided Design) model of the assembly is at our disposal and our task is to verify that the assembly has been correctly assembled, i.e. that all the elements constituting the assembly are present in the right position and at the right place. The CAD model serves as a reference. We have developed two inspection scenarios that exploit the inspection systems designed and implemented by DIOTA: (1) a scenario based on a tablet equipped with a camera, carried by a human operator for real-time interactive control, (2) a scenario based on a robot equipped with sensors (two cameras and a 3D scanner) for fully automatic control. In both scenarios, a so-called localisation camera provides in real-time the pose between the CAD model and the sensors (which allows to directly link the 3D digital model with the 2D images or the 3D point clouds analysed). We first developed 2D inspection methods, based solely on the analysis of 2D images. Then, for certain types of inspection that could not be performed by using 2D images only (typically requiring the measurement of 3D distances), we developed 3D inspection methods based on the analysis of 3D point clouds. For the 3D inspection of electrical cables, we proposed an original method for segmenting a cable within a point cloud. We have also tackled the problem of automatic selection of best view point, which allows the inspection sensor to be placed in an optimal observation position. The developed methods have been validated on many industrial cases. Some of the inspection algorithms developed during this thesis have been integrated into the DIOTA Inspect© software and are used daily by DIOTA's customers to perform inspections on industrial sites