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Artykuły w czasopismach na temat "Représentations par nuages de points 3D"
Crombez, Nathan, Guillaume Caron i El Mustapha Mouaddib. "Colorisation de nuages de points 3D par recalage dense d’images numériques". Traitement du signal 31, nr 1-2 (28.10.2014): 81–106. http://dx.doi.org/10.3166/ts.31.81-106.
Pełny tekst źródłaGirod, Luc, i Marc Pierrot-Deseilligny. "L'Égalisation radiométrique de nuages de points 3D issus de corrélation dense". Revue Française de Photogrammétrie et de Télédétection, nr 206 (19.06.2014): 3–14. http://dx.doi.org/10.52638/rfpt.2014.90.
Pełny tekst źródłaBeaudoin, Laurent, i Loïca Avanthey. "Stratégies pour adapter une chaîne de reconstruction 3D au milieu sous-marin : des idées à la pratique". Revue Française de Photogrammétrie et de Télédétection, nr 217-218 (21.09.2018): 51–61. http://dx.doi.org/10.52638/rfpt.2018.416.
Pełny tekst źródłaCléry, Isabelle, i Marc Pierrot-Deseilligny. "Une interface ergonomique de calcul de modèles 3D par photogrammétrie". Revue Française de Photogrammétrie et de Télédétection, nr 196 (15.04.2014): 40–51. http://dx.doi.org/10.52638/rfpt.2011.36.
Pełny tekst źródłaXAVIER, Frédérique, Anne DEMEESTER i Marjolaine CHATONEY. "L'éducation à la sexualité en contexte scolaire : le modèle explicatif en 3D". Revue Education, Santé, Sociétés, Vol. 8, No. 1, Volume 8, Numéro 1 (1.12.2021): 161–78. http://dx.doi.org/10.17184/eac.5354.
Pełny tekst źródłaPloyon, Estelle, Stéphane Jaillet i Olivier Barge. "Acquisition et traitements de nuages de points 3D, par des techniques légères et à faibles coûts, pour l'élaboration de MNT à haute résolution". Collection EDYTEM. Cahiers de géographie 12, nr 1 (2011): 155–68. http://dx.doi.org/10.3406/edyte.2011.1188.
Pełny tekst źródłaSamaan, Mariam, Marc Pierrot-Deseilligny, Raphaële Héno, Cyril Montoya i Sylvain Rassat. "La Photogrammétrie rapprochée pour la modélisation en 4D d'une structure archéologique". Revue Française de Photogrammétrie et de Télédétection, nr 207 (6.07.2014): 59–70. http://dx.doi.org/10.52638/rfpt.2014.15.
Pełny tekst źródłaPoreba, Martyna, i François Goulette. "Recalage rigide de relevé laser par mise en correspondance robuste basée sur des segments". Revue Française de Photogrammétrie et de Télédétection, nr 207 (24.09.2014): 3–17. http://dx.doi.org/10.52638/rfpt.2014.208.
Pełny tekst źródłaFerraz, Antonio. "DÉTECTION À HAUTE RÉSOLUTION SPATIALE DE LA DESSERTE FORESTIÈRE EN MILIEU MONTAGNEUX". Revue Française de Photogrammétrie et de Télédétection 1, nr 211-212 (6.12.2015): 103–17. http://dx.doi.org/10.52638/rfpt.2015.549.
Pełny tekst źródłaRozprawy doktorskie na temat "Représentations par nuages de points 3D"
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