An overview of view-based 2D-3D indexing methods

International audienceThis paper proposes a comprehensive overview of state of the art 2D/3D, view-based indexing methods. The principle of 2D/3D indexing methods consists of describing 3D models by means of a set of 2D shape descriptors, associated with a set of corresponding 2D views (under the assumption of a given projection model). Notably, such an approach makes it possible to identify 3D objects of interest from 2D images/videos. An experimental evaluation is also proposed, in order to examine the influence of the number of views and of the associated viewing angle selection strategies on the retrieval results. Experiments concern both 3D model retrieval and image recognition from a single view. Results obtained show promising performances, with recognition rates from a single view higher then 66%, which opens interesting perspectives in terms of semantic metadata extraction from still images/videos

Geometric guides for interactive evolutionary design

This thesis describes the addition of novel Geometric Guides to a generative Computer-Aided Design (CAD) application that supports early-stage concept generation. The application generates and evolves abstract 3D shapes, used to inspire the form of new product concepts. It was previously a conventional Interactive Evolutionary system where users selected shapes from evolving populations. However, design industry users wanted more control over the shapes, for example by allowing the system to influence the proportions of evolving forms. The solution researched, developed, integrated and tested is a more cooperative human-machine system combining classic user interaction with innovative geometric analysis. In the literature review, different types of Interactive Evolutionary Computation (IEC), Pose Normalisation (PN), Shape Comparison, and Minimum-Volume Bounding Box approaches are compared, with some of these technologies identified as applicable for this research. Using its Application Programming Interface, add-ins for the Siemens NX CAD system have been developed and integrated with an existing Interactive Evolutionary CAD system. These add-ins allow users to create a Geometric Guide (GG) at the start of a shape exploration session. Before evolving shapes can be compared with the GG, they must be aligned and scaled (known as Pose Normalisation in the literature). Computationally-efficient PN has been achieved using geometric functions such as Bounding Box for translation and scaling, and Principle Axes for the orientation. A shape comparison algorithm has been developed that is based on the principle of non-intersecting volumes. This algorithm is also implemented with standard, readily available geometric functions, is conceptually simple, accessible to other researchers and also offers appropriate efficacy. Objective geometric testing showed that the PN and Shape Comparison methods developed are suitable for this guiding application and can be efficiently adapted to enhance an Interactive Evolutionary Design system. System performance with different population sizes was examined to indicate how best to use the new guiding capabilities to assist users in evolutionary shape searching. This was backed up by participant testing research into two user interaction strategies. A Large Background Population (LBP) approach where the GG is used to select a sub-set of shapes to show to the user was shown to be the most effective. The inclusion of Geometric Guides has taken the research from the existing aesthetic focused tool to a system capable of application to a wider range of engineering design problems. This system supports earlier design processes and ideation in conceptual design and allows a designer to experiment with ideas freely to interactively explore populations of evolving solutions. The design approach has been further improved, and expanded beyond the previous quite limited scope of form exploration

Retrieval of 3-Dimensional Rigid and Non-Rigid Objects

Η παρούσα διδακτορική διατριβή εστιάζει στο πρόβλημα της ανάκτησης 3Δ αντικειμένων από μεγάλες βάσεις δεδομένων σε σχεδόν πραγματικό χρόνο. Για την αντιμετώπιση του προβλήματος αυτού, η έρευνα επικεντρώνεται σε τρία βασικά υποπροβλήματα του χώρου: (α) κανονικοποίηση θέσης άκαμπτων 3Δ μοντέλων με εφαρμογές στην ανάκτηση 3Δ αντικειμένων, (β) περιγραφή εύκαμπτων 3Δ αντικειμένων και (γ) αναζήτηση από βάσεις δεδομένων 3Δ αντικειμένων βασιζόμενη σε 2Δ εικόνες-ερώτησης. Σχετικά με το πρώτο υποπρόβλημα, την κανονικοποίηση θέσης 3Δ μοντέλων, παρουσιάζονται τρεις νέες μέθοδοι οι οποίες βασίζονται στις εξής αρχές: (α) Τριδιάστατη Ανακλαστική Συμμετρία Αντικειμένου (ROSy) και (β, γ) Διδιάστατη Ανακλαστική Συμμετρία Αντικειμένου υπολογιζόμενη επί Πανοραμικών Προβολών (SymPan και SymPan+). Όσον αφορά το δεύτερο υποπρόβλημα, αναπτύχθηκε μια μέθοδος ανάκτησης εύκαμπτων 3Δ αντικειμένων, η οποία συνδυάζει τις ιδιότητες της σύμμορφης γεωμετρίας και της τοπολογικής πληροφορίας βασιζόμενης σε γράφους, με ενιαίο τρόπο (ConTopo++). Επιπλέον, προτείνεται μια στρατηγική συνταιριασμού συμβολοσειρών, για τη σύγκριση των γράφων που αναπαριστούν 3Δ αντικείμενα. Σχετικά με το τρίτο υποπρόβλημα, παρουσιάζεται μια μέθοδος ανάκτησης 3Δ αντικειμένων, βασιζόμενη σε 2Δ εικόνες-ερώτησης, οι οποίες αντιπροσωπεύουν προβολές πραγματικών 3Δ αντικειμένων. Τα πλήρη 3Δ αντικείμενα της βάσης δεδομένων περιγράφονται από ένα σύνολο πανοραμικών προβολών και ένα μοντέλο Bag-of-Visual-Words δημιουργείται χρησιμοποιώντας τα χαρακτηριστικά SIFT που προέρχονται από αυτά. Οι μεθοδολογίες που αναπτύχθηκαν και περιγράφονται στην παρούσα διατριβή αξιολογούνται όσον αφορά την ακρίβεια ανάκτησης και παρουσιάζονται κάνοντας χρήση ποσοτικών και ποιοτικών μέτρων μέσω μιας εκτεταμένης και συνεκτικής αξιολόγησης σε σχέση με μεθόδους τρέχουσας τεχνολογικής στάθμης επάνω σε τυποποιημένες βάσεις δεδομένων.This dissertation focuses on the problem of 3D object retrieval from large datasets in a near realtime manner. In order to address this task we focus on three major subproblems of the field: (i) pose normalization of rigid 3D models with applications to 3D object retrieval, (ii) non-rigid 3D object description and (iii) search over rigid 3D object datasets based on 2D image queries. Regarding the first of the three subproblems, 3D model pose normalization, three novel pose normalization methods are presented, based on: (i) 3D Reflective Object Symmetry (ROSy) and (ii, iii) 2D Reflective Object Symmetry computed on Panoramic Views (SymPan and SymPan+). Considering the second subproblem, a non-rigid 3D object retrieval methodology, based on the properties of conformal geometry and graph-based topological information (ConTopo++) has been developed. Furthermore, a string matching strategy for the comparison of graphs that describe 3D objects, is proposed. Regarding the third subproblem a 3D object retrieval method, based on 2D range image queries that represent partial views of real 3D objects, is presented. The complete 3D objects of the database are described by a set of panoramic views and a Bag-of-Visual-Words model is built using SIFT features extracted from them. The methodologies developed and described in this dissertation are evaluated in terms of retrieval accuracy and demonstrated using both quantitative and qualitative measures via an extensive consistent evaluation against state-of-the-art methods on standard datasets

Iterative Phase Retrieval Algorithms for Scanning Transmission Electron Microscopy

Scanning transmission electron microscopy (STEM) has been extensively used for imaging complex materials down to atomic resolution. The most commonly employed STEM imaging modality of annular dark field produces easily-interpretable contrast, but is dose-inefficient and produces little to no contrast for light elements and weakly-scattering samples. An alternative is to use phase contrast STEM imaging, enabled by high speed detectors able to record full images of a diffracted STEM probe over a grid of scan positions. Phase contrast imaging in STEM is highly dose-efficient, able to measure the structure of beam-sensitive materials and even biological samples. Here, we comprehensively describe the theoretical background, algorithmic implementation details, and perform both simulated and experimental tests for three iterative phase retrieval STEM methods: focused-probe differential phase contrast, defocused-probe parallax imaging, and a generalized ptychographic gradient descent method implemented in two and three dimensions. We discuss the strengths and weaknesses of each of these approaches using a consistent framework to allow for easier comparison. This presentation of STEM phase retrieval methods will make these methods more approachable, reproducible and more readily adoptable for many classes of samples.Comment: 25 pages, 11 figures, 1 tabl

Alignment of 3D models

International audienceIn this paper we present a new method for alignment of 3D models. This approach is based on two types of symmetries of the models: the reflective symmetry and the local translational symmetry along a direction. Inspired by the work on the principal component analysis (PCA), we select the best optimal alignment axes within the PCA-axes, the plane reflection symmetry being used as a selection criterion. This pre-processing transforms the alignment problem into an indexing scheme based on the number of the retained PCA-axes. In order to capture the local translational symmetry of a shape along a direction, we introduce a new measure we call the local translational invariance cost (LTIC). The mirror planes of a model are also used to reduce the number of candidate coordinate frames when looking for the one which corresponds to the user's perception. Experimental results show that the proposed method finds the rotation that best aligns a 3D mesh

X-ray Phase Contrast Tomography : Setup and Scintillator Development

X-ray microscopy and micro-tomography (μCT) are valuable non-destructive examination methods in many disciplines such as bio-medical research, archaeometry, material science and paleontology. Besides being implemented at synchrotrons radiation sources, laboratory setups using an X-ray tube and high-resolution scintillation detector routinely provide information on the micrometre scale. To improve the image contrast for small and low-density samples, it is possible to introduce a propagation distance between sample and detector to perform propagation-based phase contrast imaging (PB-PCI). This contrast mode relies on a sufficiently coherent illumination and is characterised by the appearance of an additional intensity modulations (‘edge enhancement fringes’) around interfaces in the image. The strength of this effect depends on hardware as well as geometry parameters. This thesis describes the development of a laboratory setup for X-ray μCT with a PB-PCI option. It contains the theoretical and technical background of the setup design as well the characterization of the achieved performance.Moreover, the optimization of the PB-PCI geometry was explored both theoretically as well as experimentally for three different setups. A simple rule for finding the optimal magnification to achieve high phase contrast for edge features was deduced. The effect of the polychromatic source spectrum und detector sensitivity was identified and included into the theoretical model.Besides application and methodological studies, the setup was used to test and characterise new X-ray scintillator materials. Recently, metal halide perovskite nanocrystals (MHP NCs) have gained attention due to their outstanding opto-electronic performance. The main challenge for their use and commercialization is their low long-term stability against humidity, temperature, and light exposure. Here, a CsPbBr3 scintillator comprised of an ordered array of nanowires (NW) in an anodized aluminium oxide (AAO) membrane is presented as a promising new scintillator for X-ray microscopy and μCT. It shows a high light yield under X-ray exposure which improves with smaller NW diameter and higher NW length. In contrast to many other MHP materials this scintillator shows good stability under continuous X-ray exposure and changing environmental conditions over extended time spans of several weeks. This makes it suitable for tomography, which is demonstrated by acquiring the first high-resolution tomogram using a MHP scintillator with the presented laboratory setup

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)