Dans le domaine de l’informatique graphique, les données tridimensionnelles, généralement représentées par des maillages triangulaires, sont employées dans une grande variété d’applications (par exemple, le lissage, la compression, le remaillage, la simplification, le rendu, etc.). Cependant, ces procédés introduisent inévitablement des artefacts qui altèrent la qualité visuelle des données 3D rendues. Ainsi, afin de guider perceptuellement les algorithmes de traitement, il y a un besoin croissant d'évaluations subjectives et objectives de la qualité visuelle à la fois performantes et adaptées, pour évaluer et prédire les artefacts visuels. Dans cette thèse, nous présentons d'abord une étude exhaustive sur les différentes sources d'artefacts associés aux données numériques graphiques, ainsi que l’évaluation objective et subjective de la qualité visuelle des artefacts. Ensuite, nous introduisons une nouvelle étude sur la qualité subjective conçue sur la base de l’évaluations de la visibilité locale des artefacts géométriques, dans laquelle il a été demandé à des observateurs de marquer les zones de maillages 3D qui contiennent des distorsions visibles. Les cartes de distorsion visuelle collectées sont utilisées pour illustrer plusieurs fonctionnalités perceptuelles du système visuel humain (HVS), et servent de vérité-terrain pour évaluer les performances des attributs et des mesures géométriques bien connus pour prédire la visibilité locale des distorsions. Notre deuxième étude vise à évaluer la qualité visuelle de modèles 3D texturés, subjectivement et objectivement. Pour atteindre ces objectifs, nous avons introduit 136 modèles traités avec à la fois des distorsions géométriques et de texture, mené une expérience subjective de comparaison par paires, et invité 101 sujets pour évaluer les qualités visuelles des modèles à travers deux protocoles de rendu. Motivés par les opinions subjectives collectées, nous proposons deux mesures de qualité visuelle objective pour les maillages texturés, en se fondant sur les combinaisons optimales des mesures de qualité issues de la géométrie et de la texture. Ces mesures de perception proposées surpassent leurs homologues en termes de corrélation avec le jugement humain. / In computer graphics realm, three-dimensional graphical data, generally represented by triangular meshes, have become commonplace, and are deployed in a variety of application processes (e.g., smoothing, compression, remeshing, simplification, rendering, etc.). However, these processes inevitably introduce artifacts, altering the visual quality of the rendered 3D data. Thus, in order to perceptually drive the processing algorithms, there is an increasing need for efficient and effective subjective and objective visual quality assessments to evaluate and predict the visual artifacts. In this thesis, we first present a comprehensive survey on different sources of artifacts in digital graphics, and current objective and subjective visual quality assessments of the artifacts. Then, we introduce a newly designed subjective quality study based on evaluations of the local visibility of geometric artifacts, in which observers were asked to mark areas of 3D meshes that contain noticeable distortions. The collected perceived distortion maps are used to illustrate several perceptual functionalities of the human visual system (HVS), and serve as ground-truth to evaluate the performances of well-known geometric attributes and metrics for predicting the local visibility of distortions. Our second study aims to evaluate the visual quality of texture mapped 3D model subjectively and objectively. To achieve these goals, we introduced 136 processed models with both geometric and texture distortions, conducted a paired-comparison subjective experiment, and invited 101 subjects to evaluate the visual qualities of the models under two rendering protocols. Driven by the collected subjective opinions, we propose two objective visual quality metrics for textured meshes, relying on the optimal combinations of geometry and texture quality measures. These proposed perceptual metrics outperform their counterparts in term of the correlation with the human judgment.

Les caméras sont devenues de plus en plus communes dans les véhicules, les smartphones et les systèmes d'aide à la conduite ADAS (Advanced Driver Assistance Systèmes). Les domaines d'application de ces caméras dans le monde des systèmes intelligents de transport deviennent de plus en plus variés : la détection des piétons, les avertissements de franchissement de ligne, la navigation... La navigation basée sur la vision a atteint une certaine maturité durant ces dernières années grâce à l'utilisation de technologies avancées. Les systèmes de navigation basée sur la vision ont le considérable avantage de pouvoir utiliser directement les informations visuelles présentes dans l'environnement, sans devoir adapter le moindre élément de l'infrastructure. De plus, contrairement aux systèmes utilisant le GPS, ils peuvent être utilisés à l'extérieur ainsi qu'à l'intérieur des locaux et des bâtiments sans aucune perte de précision. C'est pour ces raisons que les systèmes basés sur la vision sont une bonne option car ils fournissent des informations très riches et précises sur l'environnement, qui peuvent être utilisées pour la navigation. Un axe important de recherche porte actuellement sur la cartographie qui représente une étape indispensable pour la navigation. Cette étape engendre une problématique de la gestion de la mémoire assez conséquente requise par ces systèmes en raison de la quantité d'informations importante collectées par chaque capteur. En effet, l'espace mémoire nécessaire pour accueillir la carte d'une petite ville se mesure en dizaines de GO voire des milliers lorsque l'on souhaite couvrir des espaces de grandes dimensions. Cela rend impossible son intégration dans un système mobile tel que les smartphones, les véhicules, les vélos ou les robots. Le défi serait donc de développer de nouveaux algorithmes permettant de diminuer au maximum la taille de la mémoire nécessaire pour faire fonctionner ce système de localisation par vision. C'est dans ce contexte que se situe notre projet qui consiste à développer un nouveau système capable de résumer une carte 3D qui contient des informations visuelles collectées par plusieurs capteurs. Le résumé sera un ensemble des vues sphériques permettant de garder le même niveau de visibilité dans toutes les directions. Cela permettrait aussi de garantir, à moindre coût, un bon niveau de précision et de rapidité lors de la navigation. La carte résumant l'environnement sera constituée d'un ensemble d'informations géométriques, photométriques et sémantiques. / Cameras 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.

Corrosion damage reduces the load-carrying capacity of bridges which poses a threat to passenger safety. The objective of this research was to reduce the resources involved in conventional bridge inspections which are an important tool in the condition assessment of bridges and to help in determining if live load testing is necessary. This research proposes a framework to link semi-automated damage detection on prestressed concrete bridge girders with the estimation of their residual flexural capacity. The framework was implemented on four full-scale corrosion damaged girders from decommissioned bridges in Virginia. 3D point clouds of the girders reconstructed from images using Structure from Motion (SfM) approach were textured with images containing cracks detected at pixel level using a U-Net (Fully Convolutional Network). Spalls were detected by identifying the locations where normals associated with the points in the 3D point cloud deviated from being perpendicular to the reference directions chosen, by an amount greater than a threshold angle. 3D textured mesh models, overlaid with the detected cracks and spalls were used as 3D damage maps to determine reduced cross-sectional areas of prestressing strands to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011). Scaling them to real-world dimensions enabled the measurement of any required dimension, eliminating the need for physical contact. The flexural capacities of a box beam and an I-beam estimated using strain compatibility analysis were validated with the actual capacities at failure sections determined from four destructive tests conducted by Al Rufaydah (2020). Along with the reduction in the cross-sectional areas of strands, limiting the ultimate strain that heavily corroded strands can develop was explored as a possible way to improve the results of the analysis. Strain compatibility analysis was used to estimate the ultimate rupture strain, in the heavily corroded bottommost layer prestressing strands exposed before the box beam was tested. More research is required to associate each level of strand corrosion with an average ultimate strain at which the corroded strands rupture. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework. / Master of Science / Corrosion damage is a major concern for bridges as it reduces their load carrying capacity. Bridge failures in the past have been attributed to corrosion damage. The risk associated with corrosion damage caused failures increases as the infrastructure ages. Many bridges across the world built forty to fifty years ago are now in a deteriorated condition and need to be repaired and retrofitted. Visual inspections to identify damage or deterioration on a bridge are very important to assess the condition of the bridge and determine the need for repairing or for posting weight restrictions for the vehicles that use the bridge. These inspections require close physical access to the hard-to-reach areas of the bridge for physically measuring the damage which involves many resources in the form of experienced engineers, skilled labor, equipment, time, and money. The safety of the personnel involved in the inspections is also a major concern. Nowadays, a lot of research is being done in using Unmanned Aerial Vehicles (UAVs) like drones for bridge inspections and in using artificial intelligence for the detection of cracks on the images of concrete and steel members. Girders or beams in a bridge are the primary longitudinal load carrying members. Concrete inherently is weak in tension. To address this problem, High Strength steel reinforcement (called prestressing steel or prestressing strands) in prestressed concrete beams is pre-loaded with a tensile force before the application of any loads so that the regions which will experience tension under the service loads would be subjected to a pre-compression to improve the performance of the beam and delay cracking. Spalls are a type of corrosion damage on concrete members where portions of concrete fall off (section loss) due to corrosion in the steel reinforcement, exposing the reinforcement to the environment which leads to accelerated corrosion causing a loss of cross-sectional area and ultimately, a rupture in the steel. If the process of detecting the damage (cracks, spalls, exposed or severed reinforcement, etc.) is automated, the next logical step that would add great value would be, to quantify the effect of the damage detected on the load carrying capacity of the bridges. Using a quantified estimate of the remaining capacity of a bridge, determined after accounting for the corrosion damage, informed decisions can be made about the measures to be taken. This research proposes a stepwise framework to forge a link between a semi-automated visual inspection and residual capacity evaluation of actual prestressed concrete bridge girders obtained from two bridges that have been removed from service in Virginia due to extensive deterioration. 3D point clouds represent an object as a set of points on its surface in three dimensional space. These point clouds can be constructed either using laser scanning or using Photogrammetry from images of the girders captured with a digital camera. In this research, 3D point clouds are reconstructed from sequences of overlapping images of the girders using an approach called Structure from Motion (SfM) which locates matched pixels present between consecutive images in the 3D space. Crack-like features were automatically detected and highlighted on the images of the girders that were used to build the 3D point clouds using artificial intelligence (Neural Network). The images with cracks highlighted were applied as texture to the surface mesh on the point cloud to transfer the detail, color, and realism present in the images to the 3D model. Spalls were detected on 3D point clouds based on the orientation of the normals associated with the points with respect to the reference directions. Point clouds and textured meshes of the girders were scaled to real-world dimensions facilitating the measurement of any required dimension on the point clouds, eliminating the need for physical contact in condition assessment. Any cracks or spalls that went unidentified in the damage detection were visible on the textured meshes of the girders improving the performance of the approach. 3D textured mesh models of the girders overlaid with the detected cracks and spalls were used as 3D damage maps in residual strength estimation. Cross-sectional slices were extracted from the dense point clouds at various sections along the length of each girder. The slices were overlaid on the cross-section drawings of the girders, and the prestressing strands affected due to the corrosion damage were identified. They were reduced in cross-sectional area to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011) and were used in the calculation of the ultimate moment capacity of the girders using an approach called strain compatibility analysis. Estimated residual capacities were compared to the actual capacities of the girders found from destructive tests conducted by Al Rufaydah (2020). Comparisons are presented for the failure sections in these tests and the results were analyzed to evaluate the effectiveness of this framework. More research is to be done to determine the factors causing rupture in prestressing strands with different degrees of corrosion. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework.