View subspaces for indexing and retrieval of 3D models

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications (Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M. Baskurt ISBN: 9780819479198 Date: 2 February 201

Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor

A novel shape descriptor for cluttered scenes is presented, the Radial Intersection Count Image (RICI), and is shown to significantly outperform the classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and, more significantly, cluttered scenes. It is also faster to compute and compare. The clutter resistance of the RICI is mainly due to the design of a novel distance function, capable of disregarding clutter to a great extent. As opposed to the SI and 3DSC, which both count point samples, the RICI uses intersection counts with the mesh surface, and is therefore noise-free. For efficient RICI construction, novel algorithms of general interest were developed. These include an efficient circle-triangle intersection algorithm and an algorithm for projecting a point into SI-like ($\alpha$, $\beta$) coordinates. The 'clutterbox experiment' is also introduced as a better way of evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are evaluated in this framework and the advantage of the RICI is clearly demonstrated.Comment: 18 pages, 16 figures, to be published in Computers & Graphic

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)

Deep learning-based 3D local feature descriptor from Mercator projections

Point clouds provide rich geometric information about a shape and a deep neural network can be used to learn effective and robust features. In this paper, we propose a novel local feature descriptor, which employs a Siamese network to directly learn robust features from the point clouds. We use a data representation based on the Mercator projection, then we use a Siamese network to map this projection into a 32-dimensional local descriptor. To validate the proposed method, we have compared it with seven state-of-the-art descriptor methods. Experimental results show the superiority of the proposed method compared to existing methods in terms of descriptiveness and robustness against noise and varying mesh resolutions

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