23 research outputs found

    Extraction robuste de primitives géométriques 3D dans un nuage de points et alignement basé sur les primitives

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    Dans ce projet, nous Ă©tudions les problĂšmes de rĂ©tro-ingĂ©nierie et de contrĂŽle de la qualitĂ© qui jouent un rĂŽle important dans la fabrication industrielle. La rĂ©tro-ingĂ©nierie tente de reconstruire un modĂšle 3D Ă  partir de nuages de points, qui s’apparente au problĂšme de la reconstruction de la surface 3D. Le contrĂŽle de la qualitĂ© est un processus dans lequel la qualitĂ© de tous les facteurs impliquĂ©s dans la production est abordĂ©e. En fait, les systĂšmes ci-dessus nĂ©cessitent beaucoup d’intervention de la part d’un utilisateur expĂ©rimentĂ©, rĂ©sultat souhaitĂ© est encore loin soit une automatisation complĂšte du processus. Par consĂ©quent, de nombreux dĂ©fis doivent encore ĂȘtre abordĂ©s pour atteindre ce rĂ©sultat hautement souhaitable en production automatisĂ©e. La premiĂšre question abordĂ©e dans la thĂšse consiste Ă  extraire les primitives gĂ©omĂ©triques 3D Ă  partir de nuages de points. Un cadre complet pour extraire plusieurs types de primitives Ă  partir de donnĂ©es 3D est proposĂ©. En particulier, une nouvelle mĂ©thode de validation est proposĂ©e pour Ă©valuer la qualitĂ© des primitives extraites. À la fin, toutes les primitives prĂ©sentes dans le nuage de points sont extraites avec les points de donnĂ©es associĂ©s et leurs paramĂštres descriptifs. Ces rĂ©sultats pourraient ĂȘtre utilisĂ©s dans diverses applications telles que la reconstruction de scĂšnes on d’édifices, la gĂ©omĂ©trie constructive et etc. La seconde question traiĂ©e dans ce travail porte sur l’alignement de deux ensembles de donnĂ©es 3D Ă  l’aide de primitives gĂ©omĂ©triques, qui sont considĂ©rĂ©es comme un nouveau descripteur robuste. L’idĂ©e d’utiliser les primitives pour l’alignement arrive Ă  surmonter plusieurs dĂ©fis rencontrĂ©s par les mĂ©thodes d’alignement existantes. Ce problĂšme d’alignement est une Ă©tape essentielle dans la modĂ©lisation 3D, la mise en registre, la rĂ©cupĂ©ration de modĂšles. Enfin, nous proposons Ă©galement une mĂ©thode automatique pour extraire les discontinutĂ©s Ă  partir de donnĂ©es 3D d’objets manufacturĂ©s. En intĂ©grant ces discontinutĂ©s au problĂšme d’alignement, il est possible d’établir automatiquement les correspondances entre primitives en utilisant l’appariement de graphes relationnels avec attributs. Nous avons expĂ©rimentĂ© tous les algorithmes proposĂ©s sur diffĂ©rents jeux de donnĂ©es synthĂ©tiques et rĂ©elles. Ces algorithmes ont non seulement rĂ©ussi Ă  accomplir leur tĂąches avec succĂšs mais se sont aussi avĂ©rĂ©s supĂ©rieus aux mĂ©thodes proposĂ©es dans la literature. Les rĂ©sultats prĂ©sentĂ©s dans le thĂšse pourraient s’avĂ©rĂ©r utilises Ă  plusieurs applications.In this research project, we address reverse engineering and quality control problems that play significant roles in industrial manufacturing. Reverse engineering attempts to rebuild a 3D model from the scanned data captured from a object, which is the problem similar to 3D surface reconstruction. Quality control is a process in which the quality of all factors involved in production is monitored and revised. In fact, the above systems currently require significant intervention from experienced users, and are thus still far from being fully automated. Therefore, many challenges still need to be addressed to achieve the desired performance for automated production. The first proposition of this thesis is to extract 3D geometric primitives from point clouds for reverse engineering and surface reconstruction. A complete framework to extract multiple types of primitives from 3D data is proposed. In particular, a novel validation method is also proposed to assess the quality of the extracted primitives. At the end, all primitives present in the point cloud are extracted with their associated data points and descriptive parameters. These results could be used in various applications such as scene and building reconstruction, constructive solid geometry, etc. The second proposition of the thesis is to align two 3D datasets using the extracted geometric primitives, which is introduced as a novel and robust descriptor. The idea of using primitives for alignment is addressed several challenges faced by existing registration methods. This alignment problem is an essential step in 3D modeling, registration and model retrieval. Finally, an automatic method to extract sharp features from 3D data of man-made objects is also proposed. By integrating the extracted sharp features into the alignment framework, it is possible implement automatic assignment of primitive correspondences using attribute relational graph matching. Each primitive is considered as a node of the graph and an attribute relational graph is created to provide a structural and relational description between primitives. We have experimented all the proposed algorithms on different synthetic and real scanned datasets. Our algorithms not only are successful in completing their tasks with good results but also outperform other methods. We believe that the contribution of them could be useful in many applications

    Implicit B-Spline Surface Reconstruction

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    The Richer Representation the Better Registration

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    International audienceIn this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach

    Multimodal Three Dimensional Scene Reconstruction, The Gaussian Fields Framework

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    The focus of this research is on building 3D representations of real world scenes and objects using different imaging sensors. Primarily range acquisition devices (such as laser scanners and stereo systems) that allow the recovery of 3D geometry, and multi-spectral image sequences including visual and thermal IR images that provide additional scene characteristics. The crucial technical challenge that we addressed is the automatic point-sets registration task. In this context our main contribution is the development of an optimization-based method at the core of which lies a unified criterion that solves simultaneously for the dense point correspondence and transformation recovery problems. The new criterion has a straightforward expression in terms of the datasets and the alignment parameters and was used primarily for 3D rigid registration of point-sets. However it proved also useful for feature-based multimodal image alignment. We derived our method from simple Boolean matching principles by approximation and relaxation. One of the main advantages of the proposed approach, as compared to the widely used class of Iterative Closest Point (ICP) algorithms, is convexity in the neighborhood of the registration parameters and continuous differentiability, allowing for the use of standard gradient-based optimization techniques. Physically the criterion is interpreted in terms of a Gaussian Force Field exerted by one point-set on the other. Such formulation proved useful for controlling and increasing the region of convergence, and hence allowing for more autonomy in correspondence tasks. Furthermore, the criterion can be computed with linear complexity using recently developed Fast Gauss Transform numerical techniques. In addition, we also introduced a new local feature descriptor that was derived from visual saliency principles and which enhanced significantly the performance of the registration algorithm. The resulting technique was subjected to a thorough experimental analysis that highlighted its strength and showed its limitations. Our current applications are in the field of 3D modeling for inspection, surveillance, and biometrics. However, since this matching framework can be applied to any type of data, that can be represented as N-dimensional point-sets, the scope of the method is shown to reach many more pattern analysis applications

    The Richer Representation the Better Registration

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    Gauss-Newton-type Techniques for Robustly Fitting Implicitly Defined Curves and Surfaces to Unorganized Data Points

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    Abstract — We describe Gauss-Newton type methods for fitting implicitly defined curves and surfaces to given unorganized data points. The methods can deal with general error functions, such as approximations to the ℓ1 or ℓ ∞ norm of the vector of residuals. Depending on the definition of the residuals, we distinguish between direct and data–based methods. In addition, we show that these methods can either be seen as (discrete) iterative methods, where an update of the unknown shape parameters is computed in each step, or as continuous evolution processes, that generate a time–dependent family of curves or surfaces, which converges towards the final result. It is shown that the data–based methods – which are less costly, as they work without the need of computing the closest points – can efficiently deal with error functions that are adapted to noisy and uncertain data. In addition, we observe that the interpretation as evolution process allows to deal with the issues of regularization and with additional constraints. I

    Courbure discrÚte : théorie et applications

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    International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor

    Combining Features and Semantics for Low-level Computer Vision

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    Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem VerstĂ€ndnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die SchĂ€tzung der Bewegung von Videokameras sind von grĂ¶ĂŸter Bedeutung fĂŒr Anwendungen, wie das autonome Fahren. Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfĂŒr sind reflektierende und texturlose OberflĂ€chen oder große Bewegungen, bei denen herkömmliche lokale Methoden hĂ€ufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, große Verzerrungen und schwierige LichtverhĂ€ltnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern. FĂŒr die binokulare Stereo SchĂ€tzung schlagen wir zuallererst vor zusammenhĂ€ngende Bereiche mit objektklassen-spezifischen DisparitĂ€ts VorschlĂ€gen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spĂ€rlichen DisparitĂ€tsschĂ€tzung und semantischen Segmentierung des Bildes erhalten. Die DisparitĂ€ts VorschlĂ€ge kodieren die Tatsache, dass die GegenstĂ€nde bestimmter Kategorien nicht willkĂŒrlich geformt sind, sondern typischerweise regelmĂ€ĂŸige Strukturen aufweisen. Wir integrieren sie fĂŒr die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen. Zweitens nutzen wir fĂŒr die 3D-Rekonstruktion die Tatsache, dass mit der GrĂ¶ĂŸe der rekonstruierten FlĂ€che auch die Wahrscheinlichkeit steigt, Objekte von Ă€hnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders fĂŒr Szenen im Freien, in denen GebĂ€ude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber Ă€hnlichkeit in der Form aufweisen. Wir nutzen diese Ă€hnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, wĂ€hrend fehlende FlĂ€chen vervollstĂ€ndigt werden, da Objekte Ă€hnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstĂ€dtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen AbhĂ€ngigkeiten zwischen Objekten. Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, prĂ€sentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusĂ€tzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein großes rezeptives Feld besitzt. FĂŒr das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm fĂŒr eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes fĂŒr das Feature Matching

    2nd International Conference on Numerical and Symbolic Computation

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    The Organizing Committee of SYMCOMP2015 – 2nd International Conference on Numerical and Symbolic Computation: Developments and Applications welcomes all the participants and acknowledge the contribution of the authors to the success of this event. This Second International Conference on Numerical and Symbolic Computation, is promoted by APMTAC - Associação Portuguesa de Mecñnica Teórica, Aplicada e Computacional and it was organized in the context of IDMEC/IST - Instituto de Engenharia Mecñnica. With this ECCOMAS Thematic Conference it is intended to bring together academic and scientific communities that are involved with Numerical and Symbolic Computation in the most various scientific area
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