3D Reconstruction Using High Resolution Implicit Surface Representations and Memory Management Strategies

La disponibilité de capteurs de numérisation 3D rapides et précis a permis de capturer de très grands ensembles de points à la surface de différents objets qui véhiculent la géométrie des objets. La métrologie appliquée consiste en l'application de mesures dans différents domaines tels que le contrôle qualité, l'inspection, la conception de produits et la rétroingénierie. Une fois que le nuage de points 3D non organisés couvrant toute la surface de l'objet a été capturé, un modèle de la surface doit être construit si des mesures métrologiques doivent être effectuées sur l'objet. Dans la reconstruction 3D en temps réel, à l'aide de scanners 3D portables, une représentation de surface implicite très efficace est le cadre de champ vectoriel, qui suppose que la surface est approchée par un plan dans chaque voxel. Le champ vectoriel contient la normale à la surface et la matrice de covariance des points tombant à l'intérieur d'un voxel. L'approche globale proposée dans ce projet est basée sur le cadre Vector Field. Le principal problème abordé dans ce projet est la résolution de l'incrément de consommation de mémoire et la précision du modèle reconstruit dans le champ vectoriel. Ce tte approche effectue une sélection objective de la taille optimale des voxels dans le cadre de champ vectoriel pour maintenir la consommation de mémoire aussi faible que possible et toujours obtenir un modèle précis de la surface. De plus, un ajustement d e surface d'ordre élevé est utilisé pour augmenter la précision du modèle. Étant donné que notre approche ne nécessite aucune paramétrisation ni calcul complexe, et qu'au lieu de travailler avec chaque point, nous travaillons avec des voxels dans le champ vectoriel, cela réduit la complexité du calcul.The availability of fast and accurate 3D scanning sensors has made it possible to capture very large sets of points at the surface of different objects that convey the geometry of the objects. A pplied metrology consists in the application of measurements in different fields such as quality control, inspection, product design and reverse engineering. Once the cloud of unorganized 3D points covering the entire surface of the object has been capture d, a model of the surface must be built if metrologic measurements are to be performed on the object. In realtime 3D reconstruction, using handheld 3D scanners a very efficient implicit surface representation is the Vector Field framework, which assumes that the surface is approximated by a plane in each voxel. The vector field contains the normal to the surface and the covariance matrix of the points falling inside a voxel. The proposed global approach in this project is based on the Vector Field framew ork. The main problem addressed in this project is solving the memory consumption increment and the accuracy of the reconstructed model in the vector field. This approach performs an objective selection of the optimal voxels size in the vector field frame work to keep the memory consumption as low as possible and still achieve an accurate model of the surface. Moreover, a highorder surface fitting is used to increase the accuracy of the model. Since our approach do not require any parametrization and compl ex calculation, and instead of working with each point we are working with voxels in the vector field, then it reduces the computational complexity

Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performanc

Covariance Based Differential Geometry Segmentation Techniques for Surface Representation Using Vector Field Framework

In this paper, the concepts of differential geometry traditionally applied to the segmentation of range maps is revisited in the context of implicit surface representation of unorganized point clouds. The paper shows that it is possible to combine covariance-based differential geometry and implicit surface representation methods to perform the segmentation of an unorganized point cloud (and not just a range map) into seven surface types. The acquisition of the point cloud data is achieved with handheld scanners used in metrology applications. The advantages of combining covariance-based differential geometry and implicit surface representation are that the segmentation does not require surface fitting nor does it require that all points be processed, thus reducing computational complexity. The segmentation approach is validated on synthetic data as well as point clouds borrowed from common datasets. Scans obtained from commercial metrologic handheld 3D sensors are also used for validation. The paper first presents the workflow commonly used for 3D scanning using handheld 3D scanners in the context of metrology. This is followed by a discussion on the different methods that are used for surface representation including the vector field, the implicit representation method exploited in this paper. Basic concepts of classical differential geometry for surface segmentation are presented. This is followed by the presentation of covariance-based differential geometry. The concepts of handheld 3D scanning, covariance-based differential geometry and implicit surface representation are then combined to achieve efficient segmentation of a point cloud into seven different surface types. Experimental results obtained on synthetic 3D data as well as real data demonstrate the segmentation approach