SHREC'16 Track: 3D Sketch-Based 3D Shape Retrieval

Sketch-based 3D shape retrieval has unique representation availability of the queries and vast applications. Therefore, it has received more and more attentions in the research community of content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate representation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the research on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketching interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D sketch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D model target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1 258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups and their retrieval performance has been evaluated using seven commonly used retrieval performance metrics. We wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote sketch-based 3D shape retrieval and its applications

Surface-based protein domains retrieval methods from a SHREC2021 challenge

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval)International audienceProteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods

Enabling European archaeological research: The ARIADNE E-infrastructure

Research e-infrastructures, digital archives and data services have become important pillars of scientific enterprise that in recent decades has become ever more collaborative, distributed and data-intensive. The archaeological research community has been an early adopter of digital tools for data acquisition, organisation, analysis and presentation of research results of individual projects. However, the provision of einfrastructure and services for data sharing, discovery, access and re-use has lagged behind. This situation is being addressed by ARIADNE: the Advanced Research Infrastructure for Archaeological Dataset Networking in Europe. This EUfunded network has developed an einfrastructure that enables data providers to register and provide access to their resources (datasets, collections) through the ARIADNE data portal, facilitating discovery, access and other services across the integrated resources. This article describes the current landscape of data repositories and services for archaeologists in Europe, and the issues that make interoperability between them difficult to realise. The results of the ARIADNE surveys on users' expectations and requirements are also presented. The main section of the article describes the architecture of the einfrastructure, core services (data registration, discovery and access) and various other extant or experimental services. The ongoing evaluation of the data integration and services is also discussed. Finally, the article summarises lessons learned, and outlines the prospects for the wider engagement of the archaeological research community in sharing data through ARIADNE

Spectral Signatures for Non-rigid 3D Shape Retrieval

This thesis addresses problems associated with computing spectral shape signatures for non-rigid 3D object retrieval. More specifically, we use spectral shape analysis tools to describe the characteristics of different 3D object representations. This thesis tries to answer whether spectral shape analysis tools can enhance classical shape signatures to improve the performance of the non-rigid shape retrieval problem. Furthermore, it describes the stages of the framework for composing non-rigid shape signatures, built from the shape Laplacian. This thesis presents four methods to improve each part of the framework for computing spectral shape signatures. The first stage comprises computing the right shape spectrum to describe 3D objects. We introduce the Kinetic Laplace-Beltrami operator which computes enhanced spectral components from 3D meshes specific to non-rigid shape retrieval and we also introduce the Mesh-Free Laplace Operator which computes more precise and robust spectral components from 3D point clouds. After computing the shape spectrum, we propose the Improved Wave Kernel Signature, a more discriminative local descriptor built from the Laplacian eigenfunctions. This descriptor is used throughout this thesis and it achieves, in most cases, state-of-the-art performances. Then, we define a new framework for encoding sparse local descriptors into shape signatures that can be compared to each other. Here, we show how to use the Fisher Vector and Super Vector to encode spectral descriptors and also how to compute dissimilarities between shape signatures using the Efficient Manifold Ranking. Furthermore, we describe the construction of the Point-Cloud Shape Retrieval of Non-Rigid Toys dataset, aimed in testing non-rigid shape signatures on point clouds, after we evidenced a lack of point-cloud benchmarks in the literature. With these ingredients, we are able to construct shape signatures which are specially built for non-rigid shape retrieval

Enabling European Archaeological Research: The ARIADNE E-Infrastructure

In the last 20 years, e-infrastructures have become ever more important for the conduct and progress of research in all branches of scientific enterprise. Increasingly collaborative, distributed and data-intensive research requires the sharing of resources (data, tools, computing facilities) via e-infrastructure as well as support for effective co-operation among research groups (ESF 2011; ESFRI 2016). Moreover there is the expectation that with large datasets ('big data'), e-infrastructure and advanced computing techniques, new scientific questions can be tackled. The archaeological research community has been an early adopter of various digital methods and tools for data acquisition, organisation, analysis and presentation of research results of individual projects. The provision of e-infrastructure and services for data sharing, discovery, access and re-use for the heritage sector is, however, lagging behind other research fields, such as the natural and life sciences. The consequence is a high level of fragmentation of archaeological data and limited capability for collaborative research across institutional and national as well as disciplinary boundaries (Aspöck and Geser 2014). This situation is being addressed by ARIADNE: the Advanced Research Infrastructure for Archaeological Dataset Networking in Europe. This e-infrastructure initiative is being promoted by a consortium of archaeological institutes, data archives and technology developers, and funded under the European Commission's Seventh Framework Programme (ARIADNE 2014a; Niccolucci and Richards 2013). ARIADNE enables archaeological data providers, large and small, to register and connect their resources (datasets, collections) to the e-infrastructure, and a data portal provides search, access and other services across the integrated resources. The portal puts into operation a proof of concept exemplar first developed under the ARENA (Archaeological Records of Europe Networked Access) project (Kenny and Richards 2005; Kilbride 2004), itself inspired by a proposal made by Hansen (1993). ARIADNE integrates resource discovery metadata using various controlled vocabularies, e.g. the W3C Data Catalogue Vocabulary (adapted for describing archaeological datasets), subject thesauri, gazetteers, chronologies, and the CIDOC Conceptual Reference Model (CRM). Based on this integration the data portal offers several ways to search and access resources made available by data providers located in different countries. ARIADNE thus acts as a broker between data providers and users and offers additional web services for products such as high-resolution images, Reflectance Transformation Imaging (RTI), 3D objects and landscapes. Employing such services in research projects or for content deposited in digital archives will greatly enhance the ability of researchers to publish, access and study archaeological content online. ARIADNE therefore represents a substantial advance for archaeology; in particular it provides a common platform where dispersed data resources can be uniformly described, discovered and accessed. It is also an essential step towards the even more ambitious goal of offering archaeologists integrated data, tools and computing resources for web-based research that creates new knowledge (e-archaeology). The next section describes the current landscape of data repositories and services for archaeologists in Europe, and the issues that make interoperability between them difficult to realise. The results of the ARIADNE user surveys undertaken to match expectations and requirements for the e-infrastructure and data portal services are then presented. The main part of the article describes ARIADNE's overall architecture, core services (data registration, discovery and access) and other extant or experimental services. A further section presents the on-going evaluation of the data integration and set of services. Finally, the article summarises some lessons already learned in the integration of data resources and services, and considers the prospects for the wider engagement of the archaeological research community in sharing data through the ARIADNE e-infrastructure and portal

From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations

Le nuage de point intelligent

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer for floor-plan’s creation and building’s construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction – particularly segmentation and classification – structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications.Les ensembles discrets de données spatiales, appelés nuages de points, forment souvent le support principal pour des scénarios d’aide à la décision. Par exemple, nous pouvons utiliser ces données comme référence pour les voitures autonomes et la navigation des robots, comme couche pour la création de plans et la construction de bâtiments, comme actif numérique pour la modélisation de l'environnement et la prédiction d’incidents... Les applications sont nombreuses et potentiellement croissantes si l'on considère les nuages de points comme des actifs de réalité numérique. Cependant, cette expansion se heurte à des limites techniques dues principalement au manque d'information sémantique au sein des ensembles de points. La création de liens avec des sources de connaissances est encore un processus très manuel, chronophage et lié à une interprétation humaine sujette à l'erreur. Cela met en évidence la nécessité d'une analyse automatisée des données relatives au domaine étudié afin de créer une information cohérente et structurée. La thèse tente clairement de résoudre les problèmes d'automatisation dans le traitement des nuages de points pour créer des environnements intelligents, c'est-àdire des copies virtuelles qui peuvent être utilisées/intégrées dans des services de raisonnement totalement autonomes. Nous abordons plusieurs problématiques liées aux nuages de points et associées à l'extraction des connaissances - en particulier la segmentation et la classification - la structuration, la visualisation et l'interaction avec les systèmes cognitifs de décision. Nous proposons de relier à la fois les propriétés des nuages de points et les connaissances formalisées pour extraire rapidement les informations pertinentes à l'aide de graphes centrés sur le domaine. La dissertation propose le concept d'une infrastructure SPC (Smart Point Cloud) qui sert d'architecture interopérable et modulaire pour un traitement unifié. Elle permet une intégration facile aux flux de travail existants et une spécialisation multidomaine grâce aux connaissances liée aux capteurs, aux connaissances analytiques ou aux connaissances de domaine. Plusieurs concepts, algorithmes, codes et supports sont fournis pour reproduire les résultats et étendre les applications actuelles.Diskrete räumliche Datensätze, so genannte Punktwolken, bilden oft die Grundlage für Entscheidungsanwendungen. Beispielsweise können wir solche Daten als Referenz für autonome Autos und Roboternavigation, als Ebene für die Erstellung von Grundrissen und Gebäudekonstruktionen, als digitales Gut für die Umgebungsmodellierung und Ereignisprognose verwenden... Die Anwendungen sind zahlreich und nehmen potenziell zu, wenn wir Punktwolken als Digital Reality Assets betrachten. Allerdings stößt diese Erweiterung vor allem durch den Mangel an semantischen Informationen innerhalb von Punkt-Ensembles auf technische Grenzen. Die Verbindung von Wissensquellen ist immer noch ein sehr manueller und zeitaufwendiger Prozess, der unter fehleranfälliger menschlicher Interpretation leidet. Dies verdeutlicht den starken Bedarf an domänenbezogenen Datenanalysen, um eine kohärente und strukturierte Information zu schaffen. Die Arbeit versucht eindeutig, Automatisierungsprobleme in der Punktwolkenverarbeitung zu lösen, um intelligente Umgebungen zu schaffen, d.h. virtuelle Kopien, die in vollständig autonome Argumentationsdienste verwendet/integriert werden können. Wir befassen uns mit Punktwolkenfragen im Zusammenhang mit der Wissensextraktion - insbesondere Segmentierung und Klassifizierung - Strukturierung, Visualisierung und Interaktion mit kognitiven Entscheidungssystemen. Wir schlagen vor, sowohl Punktwolkeneigenschaften als auch formalisiertes Wissen zu verbinden, um schnell relevante Informationen mithilfe von domänenzentrierten Grafiken zu extrahieren. Die Dissertation liefert das Konzept einer Smart Point Cloud (SPC) Infrastruktur, die als interoperable und modulare Architektur für eine einheitliche Verarbeitung dient. Es ermöglicht eine einfache Integration in bestehende Workflows und eine multidimensionale Spezialisierung durch Gerätewissen, analytisches Wissen oder Domänenwissen. Konzepte, Algorithmen, Code und Materialien werden zur Verfügung gestellt, um Erkenntnisse zu replizieren und aktuelle Anwendungen zu erweitern